The development of robust, food-grade microbial chassis with tailored metabolic functions is critical for advancing synthetic biology applications in health and nutrition. Here, we report a dual genome engineering strategy that integrates CRISPR-Cas9-mediated knock-in with Cre/loxP-driven genome reduction to streamline the genome of Lactococcus lactis NZ9000 and enable stable expression of a high-activity uricase variant. The resulting strain, NZ9000::UAT-ΔD6, demonstrated enhanced enzymatic performance in vitro, achieving 2.34 U/mL activity and complete degradation of ∼500 μM urate within 20 h. Beyond improved catalytic output, this dual-system approach established a genetically stable and biosafe probiotic chassis with moderate colonization capacity in the murine gut. The integration of CRISPR-Cas and Cre/loxP techniques in this work is intended to enhance the expression of heterologous genes in the chassis strain, while providing a versatile platform for the rational design of food-grade probiotics and offering a general strategy for constructing living biotherapeutic agents with targeted metabolic activities.

Arctic permafrosts are rapidly thawing in response to climate change, stimulating microbial activity and release of additional greenhouse gases, such as methane. Recently, catechin amendment of thawed permafrost soil microcosms demonstrated >80% decrease in methane production over 35 days compared to unamended controls, with metagenome, metatranscriptome and metabolome analyses revealing a shift in prokaryotic carbon metabolism from a syntrophic network feeding methanogens to one dominated by catechin fermentation. Here we leverage these same data to investigate potential virus impacts on this microbial community metabolism shift. In total, 900 DNA virus operational taxonomic units (vOTUs) were identified as actively lytic based on transcribed structural and lysis genes. Of these, 41% were predicted to infect at least one of 56 transcriptionally active prokaryote genera representing 13 phyla. The most active vOTUs were predicted to infect key catechin-degrading genera including Clostridium and undescribed Bacillota genus JAGFXR01. Temporally, viral communities responded later than prokaryotes to catechin amendment, reflecting a virus production lag to targeting key microbial responders. An induced prophage represented by a vOTU predicted to infect JAGFXR01 dominated the catechin-amended viral community response. It reached abundances 20-156 times higher than its host, suggesting intense viral lysis that could release degraded catechin intermediates. Indeed, catechin intermediate gene expression in non-catechin-degraders were elevated in catechin-amended samples, suggesting JAGFXR01 lysis products were taken up by non-catechin-degraders as part of community carbon metabolism rewiring. Together, these results potentially place viruses at the heart of carbon rewiring that modulates ecosystem outputs of climate-critical thawing permafrosts.

Analyzing biological networks demands scalable annotation tools, yet existing methods fall short in clustering power, statistical flexibility, and broad data compatibility. We introduce RISK (Regional Inference of Significant Kinships), a next-generation tool that overcomes these challenges by integrating community detection algorithms, rigorous overrepresentation analysis, and a modular architecture that supports diverse network types. RISK identifies biologically coherent relationships within networks and generates publication-ready visualizations, as demonstrated by its ability to resolve compact functional modules in Saccharomyces cerevisiae protein–protein interaction and genetic interaction networks. Its application to a high-energy physics citation network reveals structured relationships among research subfields, highlighting its versatility beyond biological systems. As biological and interdisciplinary networks increase in size and complexity, RISK’s scalability and adaptability make it a powerful solution for modern network analysis.

Pooled CRISPR screens using single-cell RNA sequencing (scRNA-seq) have emerged as powerful tools to uncover gene function, map regulatory networks, and identify genetic interactions. However, the inherent sparsity of bacterial scRNA-seq data has posed a major challenge toward applying these approaches to bacteria. Here, we present mapSPLiT, a pooled bacterial CRISPR activation and interference (CRISPRa/i) screening platform that enables large-scale experiments that simultaneously link hundreds of perturbations to their corresponding single-cell transcriptomes. By targeting 52 known or putative transcription factors with 118 perturbations in a pooled experiment, we expanded the E. coli regulatory network map, determined the function of putative regulators, and identified emergent global phenotypes. By targeting combinations of transcription factors simultaneously, we uncovered genetic interactions and regulatory logic between them. Mapping regulatory networks for carbon utilization in P. putida revealed control points that could expand metabolic flexibility and improve biomanufacturing. Together, these results establish mapSPLiT as a generalizable platform for single‑cell functional genomics in bacteria.

Microbiomes, complex communities of microorganisms and their genetic material, hold immense potential for addressing global challenges in diverse sectors, including healthcare, agriculture, and bioproduction. Engineering these intricate ecosystems, however, necessitates a comprehensive understanding of the complex web of microbial interactions. The emergence of machine learning (ML) has revolutionized microbiome research, offering powerful tools to analyze massive data sets, uncover hidden patterns, and predict microbial behavior. ML algorithms have demonstrated remarkable success in identifying and characterizing microbial communities, predicting interactions between organisms and optimizing the design of microbial communities for specific functions. This Perspective examines the transformative applications of ML in the context of microbiome engineering, encompassing both microbiome data analysis and the targeted manipulation of microbial communities. These techniques employ a variety of strategies, including the manipulation of quorum sensing molecules, antimicrobial peptides, growth conditions, the introduction of probiotics, and the utilization of bacteriophages. By integrating ML with experimental approaches, researchers are pushing the boundaries of microbiome engineering, paving the way for novel applications in diverse fields. However, it is important to acknowledge the challenges that ML algorithms face, such as the limited availability of high-quality, large-scale data sets, the inherent complexity of biological systems, and the need for improved integration of experimental and computational methods. This perspective further discusses the future perspectives of the field, highlighting expected developments in data generation, algorithm development, and interdisciplinary collaboration. These advancements hold the key to unlocking the full potential of microbial communities for addressing pressing global challenges.

Lactoferrin (LF) plays a positive role in attenuating aging. In this study, LF obtained using different processing methods (freeze-dried: F and spray-dried: S) and its gastrointestinal digesta (XF and XS) were supplemented in d-gal-induced mice to explore their antiaging effects. The results showed that LF and its digesta (LFs) effectively ameliorated cognitive decline. Mechanistically, LFs prevented neuronal and synaptic injury by restoring redox balance, inhibiting the activation of microglia and astrocytes, and activating the cAMP-response element binding protein (CREB)/brain-derived neurotrophic factor (BDNF) pathway. Additionally, LFs increased the tight junction proteins and mucin-2, regulated the gut microbiota, particularly enriching bacteria in Firmicutes and restoring the Firmicutes/Bacteroidota ratio to maintain intestinal homeostasis. Meanwhile, LFs altered phospholipids (PLs) and other metabolites involved in glycerophospholipid metabolism such as arachidonic acid. Correlation analysis showed a significant association among metabolites, microbiota, and behaviors. These results indicated that LF and especially its digesta exert antiaging effects through multitarget pathways involving neuronal protection, neuroinflammation suppression, and microbiota–gut–brain axis regulation.

Rubisco catalyzes the CO2 fixation step in the dark reactions of photosynthesis. Transgenic expression of better-performing Rubisco orthologs in plants or discovery of improved mutants of Rubisco via protein engineering could theoretically accelerate plant growth and improve crop yields. However, efforts to heterologously express or engineer Rubisco are frequently stymied by the chaperone-dependent folding and assembly of the Rubisco holoenzyme, a process that can be disrupted by changes to Rubisco’s sequence. Elucidation of the effects that alterations to Rubisco’s sequence impose upon its biogenesis is hampered by reliance upon low-throughput methods for verification of Rubisco assembly. Here, we report the engineering of a genetically encoded biosensor to sense the assembly of Form I Rubiscos in E. coli. We show that the biosensor can detect the RbcS-dependent assembly of cyanobacterial Rubisco orthologs, the formation of chaperone-stabilized RbcL oligomeric assembly intermediates, and differences in assembly caused by mutations to the RbcL sequence. Additionally, we perform a large-scale examination of the relative assembly levels of a ∼7500-member Halothiobacillus neapolitanus RbcL mutant library by adapting the biosensor for use with phage-assisted noncontinuous selection. Our experiment predicts that the majority (>90%) of examined RbcL mutations exert a negative effect on assembly, lending support to the hypothesis that Rubisco biogenesis constrains both its natural evolution and improvement by protein engineering.

Recombinant adeno-associated viral (rAAV) vectors are a leading platform for in vivo gene therapy, valued for their excellent safety, broad serotype diversity, and scalable production. Targeted delivery through capsid display of ligands holds great promise, yet current retargeting strategies often rely on extensive capsid re-engineering and restrict the use of ligands incompatible with intracellular expression systems. Here, we present a modular AAV retargeting platform that, for the first time, employs the SpyTag/SpyCatcher system via genetic integration into the AAV2 capsid. SpyTag is a small peptide that forms a covalent, irreversible bond with its protein partner, SpyCatcher, allowing site-specific ligand coupling under physiological conditions. Inserting SpyTag into surface-exposed capsid sites enabled postassembly functionalization of AAVs with SpyCatcher-fused targeting proteins. As proof of concept, we used SpyCatcher fusions with designed ankyrin repeat proteins (DARPins) specific for EGFR, EpCAM, and HER2. This conferred highly specific transduction of corresponding cancer cell lines with minimal off-target activity. Therapeutic potential was demonstrated by delivering a suicide gene, inducing selective cancer cell killing upon prodrug administration. This “one-fits-all” platform allows rapid and flexible retargeting without significantly altering the underlying vectors genome or production process. It supports the incorporation of large or complex ligands not amenable to genetic fusion and facilitates high-throughput preclinical evaluation strategies. By uniting capsid engineering with modular ligand display, our approach provides a scalable and versatile framework for precision gene delivery, broadening the applicability of rAAV in both therapeutic and discovery settings.

The rising rate of drug-resistant fungal infections and the emergence of fungal pathogens with intrinsic resistance phenotypes are a growing concern. The close evolutionary distance between mammals and fungi complicates the design of new antifungals and increases the chances of toxic off-target effects. As such, antifungal drug development usually focuses on fungal-specific proteins when considering potential new targets. Ideal drug targets should mediate essential cell processes and be highly sensitive to inhibition. Targeted gene repression can serve as a model for drug-mediated inhibition and for determining the dosage-sensitivity profile of genes of interest. In the fungal pathogen Candida albicans, classical approaches for gene repression can be labor-intensive and limited to one genetic background due to low throughput. Here, we adapt pooled CRISPRi in C. albicans for the first time and exploit this technique for large-scale functional genomic analysis. Through pooled CRISPRi screening, we test the repression sensitivity of over a hundred essential genes conserved in fungi but absent in humans in a single flask, and successfully identify highly dosage-sensitive genes across multiple cell components and pathways. We further extended our analysis to ten diverse environmental conditions, allowing us to measure how the environment influences dosage-sensitivity profiles. Finally, we extend our experiments to two clinical drug-resistant C. albicans strain backgrounds and demonstrate that many of the fitness defects we observed are conserved in resistant clinical isolates. Together, our results highlight a set of genes that are highly dosage-sensitive across different genetic and environmental contexts, making them attractive targets for further investigation. By facilitating rapid, efficient large-scale functional genomics assays across diverse genetic backgrounds, CRISPRi pooled screening will open new frontiers in C. albicans biology.

Invertebrates, as the majority of macroscopic species on the Earth, are important resources for natural products. Chemical investigations of animals can date back to the early 20th century and have led to the discovery of thousands of compounds with diverse biological functions. These natural products can be structurally classified as terpenoids, polyketides, and alkaloids. Additionally, many compounds have been isolated from symbionts, leading to the widespread belief that animals lack the capability for secondary metabolism. Recent biochemical studies challenge this notion, revealing great potential for animal biosynthesis research. Animals possess larger genomes and more complex metabolic pathways, suggesting untapped biosynthetic potential. In contrast to microorganisms, studies on the biosynthesis of natural products in animals remain limited. Characterized genes represent only a small fraction of their vast genomes. The discovery of biosynthetic gene clusters suggests that the methods used to mine the biosynthetic genes of microorganisms may also be applicable to animals. The characterization of 4-vinylanisole in locusts demonstrates that the pathways lacking clear core biosynthesis enzymes still require multidisciplinary experimental approaches. In summary, further biosynthesis studies will expand methodological approaches and accelerate the characterization of remaining natural product pathways.