A classic problem in metabolism is that fast-proliferating cells use seemingly wasteful fermentation for energy biogenesis in the presence of sufficient oxygen. This counterintuitive phenomenon, known as overflow metabolism or the Warburg effect, is universal across various organisms. Despite extensive research, its origin and function remain unclear. Here, we show that overflow metabolism can be understood through growth optimization combined with cell heterogeneity. A model of optimal protein allocation, coupled with heterogeneity in enzyme catalytic rates among cells, quantitatively explains why and how cells choose between respiration and fermentation under different nutrient conditions. Our model quantitatively illustrates the growth rate dependence of fermentation flux and enzyme allocation under various perturbations and is fully validated by experimental results in Escherichia coli. Our work provides a quantitative explanation for the Crabtree effect in yeast and the Warburg effect in cancer cells and can be broadly used to address heterogeneity-related challenges in metabolism.

Converting lignocellulose into bioelectricity through a bioelectrocatalytic system (BES) has emerged as a promising approach to addressing environmental pollution and energy regeneration challenges. However, practical application of BES is significantly constrained by the fact that the electroactive biocatalyst Shewanella oneidensis lacks the essential metabolic pathways and enzymes required for utilizing lignocellulose for cell growth and power generation. Here, to realize clean electricity production from lignocellulose hydrolysate, an artificial microbial consortium comprising S. oneidensis, Lactococcus lactis, and Bacillus subtilis was developed. In this consortium, L. lactis is responsible for converting glucose into lactate; B. subtilis metabolizes glucose and xylose into riboflavin; and S. oneidensis then employs lactate as an electron donor and riboflavin as an electron shuttle to facilitate electricity generation. Subsequently, to increase substrate conversion efficiency of the microbial consortium, three key genes codY, ribA, and dld encoding lactate dehydrogenase, GTP cyclohydrolase, and d-lactate dehydrogenase, were expressed in L. lactis, B. subtilis, and S. oneidensis, respectively, which accelerated glucose-to-lactate conversion, riboflavin synthesis, and lactate metabolism. Also, to accelerate the extracellular electron transfer (EET) capacity of the microbial consortium, the cyc2 gene from Acidithiobacillus ferrooxidans encoding the outer membrane c-type cytochrome was further expressed in S. oneidensis. Finally, to further enhance the interfacial EET capability of the microbial consortium, a 3D microbiota biohybrid system S7L1B1@CF&GO consisting of carbon felts and graphene oxide was developed to reduce the internal resistance of BES. The results showed that the artificial biohybrid system could obtain a maximum power density of ∼739.40 mW m–2 using lignocellulosic hydrolysate as the carbon source. This system expands the range of carbon sources available to S. oneidensis for efficient power generation from the lignocellulosic hydrolysate.

Engineered living materials (ELM) at the multicelluar level represent an innovation that promises programmable properties for biomedical, environmental, and consumer applications. However, the rational tuning of the mechanical properties of such ELMs from first principles remains a challenge. Here we use synthetic cell-cell adhesins to systematically characterize how rheological and viscoelastic properties of multicellular materials made from living bacteria can be tuned via adhesin strength, cell size and shape, and adhesion logic. We confirmed that the previous results obtained for non-living materials also apply to bacterial ELMs. Additionally, the incorporation of synthetic adhesins, combined with the adaptability of bacterial cells in modifying various cellular parameters, now enables novel and precise control over material properties. Furthermore, we demonstrate that rheology is a powerful tool for actively shaping the microscopic structure of ELMs, enabling control over cell aggregation and particle rearrangement, a key feature for complex material design. These results deepen our understanding of tuning the viscoelastic properties and fine structure of ELMs for applications like bioprinting and microbial consortia design including natural systems.

Nature uses structural variations on protein folds to fine-tune the geometries of proteins for diverse functions, yet deep learning-based de novo protein design methods generate highly regular, idealized protein fold geometries that fail to capture natural diversity. Here, using physics-based design methods, we generated and experimentally validated a dataset of 5,996 stable, de novo designed proteins with diverse non-ideal geometries. We show that deep learning-based structure prediction methods applied to this set have a systematic bias towards idealized geometries. To address this problem, we present a fine-tuned version of Alphafold2 that is capable of recapitulating geometric diversity and generalizes to a new dataset of thousands of geometrically diverse de novo proteins from 5 fold families unseen in fine-tuning. Our results suggest that current deep learning-based structure prediction methods do not capture some of the physics that underlie the specific conformational preferences of proteins designed de novo and observed in nature. Ultimately, approaches such as ours and further informative datasets should lead to improved models that reflect more of the physical principles of atomic packing and hydrogen bonding interactions and enable improved generalization to more challenging design problems.

Ralstonia solanacearum, a Gram-negative bacterium, is the causative agent of bacterial wilt, a devastating disease affecting a wide range of economically important crops worldwide. This study explores the dynamic interactions between Ralstonia solanacearum and its host plants, emphasizing key mechanisms underlying infection and host response. The pathogen initiates infection through root wounds or natural openings, rapidly colonizing xylem vessels where it forms biofilms that disrupt water and nutrient transport. Its virulence is driven by cell wall-degrading enzymes and effector proteins delivered via a Type III secretion system, which subvert plant immune responses and facilitate systemic spread. In turn, host plants activate hormonal and stress-related defense pathways, though these are often manipulated by the pathogen, leading to disease progression and reduced productivity. This review highlights critical gaps in our understanding of molecular host-pathogen interactions and the role of environmental conditions in disease development. Addressing these gaps is vital for improving management strategies, with breeding for resistance and advanced biotechnological tools offering promising solutions to combat bacterial wilt and support sustainable agriculture. Future research should focus on leveraging genetic insights to enhance host resistance, employing advanced biotechnological tools to develop crop varieties with enhanced resistance to Ralstonia solanacearum, thereby promoting sustainable agriculture and strengthening global food security.

The rapid growth of genomic and metagenomic data has underscored the pressing need for advanced computational tools capable of deciphering complex biological sequences. In this study, we introduce GENERanno, a compact yet powerful genomic foundation model specifically optimized for metagenomic annotation. Trained on an extensive dataset comprising 715 billion base pairs (bp) of prokaryotic DNA, GENERanno employs a transformer encoder architecture with 500 million parameters, enabling bidirectional attention over sequences up to 8192 nucleotides at single-nucleotide resolution. This design addresses key limitations of existing methods, including the inability of traditional Hidden Markov Models (HMMs) to handle fragmented DNA sequences, as well as the suboptimal tokenization schemes of current genomic foundation models that compromise fine-grained analysis. To evaluate the model performance, we curated the Prokaryotic Gener Tasks, a biologically meaningful benchmark encompassing gene fitness prediction, antibiotic resistance prediction, gene classification, and taxonomic classification. Across these tasks, GENERanno consistently outperforms its counterparts, establishing itself as a leading genomic foundation model in the prokaryotic domain. For metagenomic annotation, GENERanno achieves superior accuracy compared to traditional HMM-based methods (e.g., GLIMMER3, GeneMarkS2, Prodigal) and recent LLM-based approaches (e.g., GeneLM), while demonstrating exceptional generalization ability on archaeal genomes. Notably, GENERanno pioneers the prediction of pseudogenes based solely on sequence data, leveraging its contextual understanding to differentiate non-functional sequences from active coding regions. Overall, GENERanno represents a significant advancement in genomic foundation modeling, bridging the gap between large-scale sequence analysis and fine-grained biological insights. By providing a versatile tool for metagenomic annotation and broader genomic exploration, this work lays the groundwork for future research in functional genomics and related fields. https://github.com/GenerTeam/GENERanno