Trends in biotechnology最新文献

A biofoundry integrates laboratory automation with Design-Build-Test-Learn (DBTL) workflows to accelerate strain development for sustainable manufacturing. Quantifying the economic efficiency of automated processes remains challenging. Here, we define the robot-assisted module (RAM) as a plug-and-play unit for constructing workflow and apply the Experiment Price Index (EPI), a standardized metric that combines time and cost per sample to evaluate and optimize synthetic biology workflows. Using EPI calculation and RAMs, we developed four workflows for strain development: guide (g)RNA cloning, genome editing, DNA assembly, and sample analysis. EPI identified workflow bottlenecks, elimination of redundancies, and assessment of techno-economic tradeoffs. We further extended the EPI framework on techno-economic assessment (TEA) by estimating return on investment (ROI) and payback periods for biofoundry operations at varying project scales. Our results demonstrate EPI for cost-effective experimental planning and scalable biofoundry deployment. Beyond strain engineering, EPI serves as a universal tool for evaluating automation efficiency across biotechnology.

Piezoelectric scaffolds are emerging as promising therapeutic strategies for musculoskeletal regeneration. These materials convert mechanical forces, ranging from intrinsic motion to focused ultrasound (US) force, into localized electrical cues for tissue-specific musculoskeletal regeneration. Mechanistically, piezoelectric-converted electrical energy activates mechanosensitive and voltage-gated channels that trigger early regenerative signaling pathways. In this review, we describe the fundamental principles of the piezoelectric material class that focus on dipole alignment, geometry, and activation paradigms to culminate in their differential effects on the regeneration of musculoskeletal tissues. We also discuss lead-free platforms, closed-loop systems, as well as printable constructs capable of delivering wire-free electrical stimulation (ES). Finally, we discuss current translational challenges and future directions and practical steps toward clinical adoption of piezoelectric scaffolds.

Engineering infrared light sensitivity in the blind human retina could restore visual function in patients with regional retinal degeneration. However, current approaches are complex and contain non-human biological components. Using rational protein design, we engineered human transient receptor potential vanilloid 1 (hTRPV1) channels (Δ786-840) with temperature sensitivity that shifted from 45 to 41°C, which enabled near-infrared (NIR) light-induced heat activation of mammalian cells at close to physiological temperatures. When expressed in ganglion cells of human retinal explants, Δ786-840 TRPV1 generated robust spiking responses to brief NIR light-induced temperature transients. In addition, increasing intensity of radiation evoked graded responses correlating with increasing firing frequencies. Unlike previous approaches, which used non-human TRPV1 channels, risking immune reactions, and a multicomponent system that poses barriers to clinical implementation, this single-component human-derived approach eliminates immunogenicity concerns, addressing a major challenge to clinical translation, and allows gene delivery using adeno-associated virus (AAV) vectors.

Despite being the most commonly mutated proteins in cancer, oncogenic RAS proteins remain largely untapped as pharmacological targets. Here, we report a synthetic cancer-killing platform, termed 'RAS-activated cancer killing (RACK)' system. Leveraging a transcriptional sensor designed to detect oncogenic RAS signals with high specificity, RACK achieves targeted identification and elimination of RAS-mutant cancer cells. RACK can potently target a range of RAS and non-RAS mutants, including, but not limited to KRAS, NRAS, BRAF, and RTKs. Notably, RACK can maintain its efficacy against cancer cells that have developed acquired resistance, outperforming conventional inhibitors. In vivo, RACK selectively inhibits RAS-mutant tumor growth in xenograft models, including those intractable by allele-specific inhibitors. Furthermore, the modular design of RACK allows rational optimization of promoter inputs and therapeutic outputs. Collectively, RACK introduces a pioneering drug approach for detecting and treating RAS-mutant cancers, paving the way for overcoming challenges associated with currently undruggable cancer targets.

Advances in machine learning for structural biology have dramatically enhanced our capacity to predict protein-protein interactions (PPIs). Here, we review recent developments in the computational prediction of PPIs, particularly focusing on innovations that enable interaction predictions that have no precedence in nature, termed de novo. We discuss novel machine learning algorithms for PPI prediction, including approaches based on co-folding and atomic graphs. We further highlight methods that learn from molecular surfaces, which can predict PPIs not found in nature including interactions induced by small molecules. Finally, we explore the emerging biotechnological applications enabled by these predictive capabilities, including the prediction of antibody-antigen complexes and molecular glue-induced PPIs, and discuss their potential to empower drug discovery and protein engineering.

Bacterial extracellular vesicles (BEVs) are nanoscale spherical particles with lipid bilayer membranes containing diverse functional components from their parent bacteria. They exert pivotal effects on bacteria-bacteria and host-bacteria communication. Analyzing the dynamic changes in the production and composition of BEVs provides insights into the relationship between gut microbiota and host health, offering valuable perspectives for diagnosing various gastrointestinal diseases. Furthermore, BEVs can be employed as natural medications and drug delivery vehicles for inflammation management, cancer treatment, and vaccine development. This review summarizes the structural composition, generation mechanism, and biomedical applications of BEVs, emphasizing recent advances in immune regulation, gut microbiota modulation, and the clinical translation challenges associated with gastrointestinal diseases.

Double-stranded RNA (dsRNA)-based pesticides face challenges in stability, scalability, efficient uptake, and broad applicability. Here, we present self-assembled RNA nanostructures (SARNs), engineered to load pools of functional siRNAs with motifs that enhance hydrophobicity and elasticity, and enable both immediate and sustained siRNA release for efficient RNAi. SARNs improve RNA stability and delivery in plants and in model pests with chewing mouthparts (Tribolium castaneum) and piercing-sucking mouthparts (Nilaparvata lugens). Compared with dsRNA, SARNs demonstrated superior RNAi efficiency in T. castaneum and N. lugens, achieving significantly higher downregulation efficacy and mortality in both species. In addition, SARNs, which self-assemble from single-stranded (ss)RNA molecules, can be transcribed in Escherichia coli for scalable production. We further establish a framework for the laboratory-to-field transition of SARNs. This engineered RNA platform offers an efficient, scalable, cost-effective solution for RNA-based gene silencing, advancing applications in agriculture and biomedicine.

Cross-reactivity is viewed as a highly undesirable property of antibodies, with efforts focused on achieving high specificity and minimal crosstalk. Yet, in selective arrays, cross-reactivity can be a strength. The ability to bind to a range of antigens can be powerful if used strategically to distinguish complex samples. Chemometric and chemolfactory arrays rely on cross-reactivity to deliver powerful assays with capabilities that often outperform those of specific assays. Here, we argue that there is a unique opportunity to exploit the cross-reactivity of antibodies by using them in array similar to the mode of chemolfactory arrays. Embracing this shift could transform biosensing, offering scalable tools for multiplexed detection, especially in settings where speed, cost, and adaptability are critical.

Lymph nodes (LNs) are a vital component of the adaptive immune system as they have a key role in antigen presentation and regulation of immune responses. However, preclinical models that accurately mimic the complexity and spatial organization of LNs remain a significant unmet need for the study of LN biology. Here, we leveraged the use of biomaterials to significantly extend the lifespan of patient-derived LN explants ex vivo. Hydrogel-embedded LN explants preserved the cellular composition and maintained the intricate spatial organization of the LN. This enabled the LN explants to retain functional responsiveness, as demonstrated by their ability to mount immune responses after exposure to tumor antigens or SARS-CoV-2 mRNA vaccine. The LN explant models developed in this study offer a robust and physiologically relevant platform for studying immune responses ex vivo, facilitating the development of vaccines and immunotherapies in the context of cancer and infectious diseases.

Rare sugars are valuable for food and pharmaceutical applications. D-Mannose, a low-calorie sweetener, is traditionally produced via chemical extraction from plant biomass, which is unsustainable, while enzymatic methods suffer from low yields due to equilibrium limitations. Here, we demonstrate that Escherichia coli can naturally synthesize D-mannose from D-glucose through a phosphorylation-isomerization-dephosphorylation pathway. We enhanced D-mannose production by deleting competing pathways and overexpressing key biosynthetic genes. Unexpectedly, due to the promiscuous activity of the phosphatase HxpB, which dephosphorylates both D-mannose-6-phosphate (M6P) and D-sedoheptulose-7-phosphate (S7P), the engineered strain also produced D-sedoheptulose, a non-sweet rare sugar that inhibits C6 sugar consumption. Further metabolic engineering improved D-sedoheptulose production. These optimizations enabled the development of a co-production strain capable of producing both sugars with tunable ratios. By leveraging this unique sugar combination, our approach provides a sustainable route to rare sugar biosynthesis and opens new possibilities for functional food design and metabolic regulation.