Biotechnology advances最新文献_第5页

Integrating multi-omics and artificial intelligence fuels advanced target identification and drug discovery 整合多组学和人工智能推动了先进的目标识别和药物发现

IF 12.5 1区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biotechnology advances

Pub Date : 2025-12-27 DOI: 10.1016/j.biotechadv.2025.108785

Hang Chen , Ying Shi , Meiling Yuan , Huihui Li , Xiaowei Liu

Target identification is pivotal for developing novel therapeutics in cancer and other diseases. Traditional experiment screening methods are constrained by low throughput and the complexity of biological systems. Multi-omics technologies offer a transformative solution by providing comprehensive, multi-dimensional insights into molecular mechanisms. However, the exponential growth of multi-omics data necessitates efficient computational algorithms for dimensionality reduction and unravel the intricate biological processes. Artificial intelligence (AI) has emerged as a powerful tool capable of analyzing complementary multi-modal data streams. The integration of multi-omics technologies and AI algorithms has revolutionized target identification and drug discovery. This review highlights prevalent omics techniques and their role in target identification and drug discovery, outlines key machine learning (ML) classifications, and describes the integration of multi-omics with AI. We explore the applications of AI-driven multi-omics in various stages of drug discovery, including target identification, target validation, lead optimization, as well as clinical evaluation, underscoring the transformative potential of this approach. Additionally, we discuss the challenges associated with this integrative strategy and future trends in the field. As the integration of multi-omics and AI continues to expand, we anticipate a paradigm shift in target identification and drug discovery, paving the way for more precise and effective therapies.

目标识别是开发癌症和其他疾病的新疗法的关键。传统的实验筛选方法受到低通量和生物系统复杂性的限制。多组学技术通过提供对分子机制的全面、多维的见解，提供了一种变革性的解决方案。然而，多组学数据的指数增长需要有效的计算算法来降维和揭示复杂的生物过程。人工智能（AI）已经成为一种强大的工具，能够分析互补的多模态数据流。多组学技术和人工智能算法的整合已经彻底改变了目标识别和药物发现。本文重点介绍了流行的组学技术及其在靶标识别和药物发现中的作用，概述了关键的机器学习（ML）分类，并描述了多组学与人工智能的集成。我们探讨了人工智能驱动的多组学在药物发现的各个阶段的应用，包括靶点识别、靶点验证、先导物优化以及临床评估，强调了这种方法的变革潜力。此外，我们还讨论了与该领域的综合战略和未来趋势相关的挑战。随着多组学和人工智能的整合不断扩大，我们预计在目标识别和药物发现方面将发生范式转变，为更精确和有效的治疗铺平道路。

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引用次数: 0

Geometric deep learning assists protein engineering. Opportunities and Challenges 几何深度学习有助于蛋白质工程。机遇与挑战

IF 12.5 1区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biotechnology advances

Pub Date : 2025-12-26 DOI: 10.1016/j.biotechadv.2025.108790

Julián García-Vinuesa , Jorge Rojas , Nicole Soto-García , Nicolás Martínez , Diego Alvarez-Saravia , Roberto Uribe-Paredes , Mehdi D. Davari , Carlos Conca , Juan A. Asenjo , David Medina-Ortiz

Protein engineering is experiencing a paradigmatic transformation through the integration of geometric deep learning (GDL) into computational design workflows. While traditional approaches such as rational design and directed evolution have achieved significant progress, they remain constrained by the vastness of sequence space and the cost of experimental validation. GDL overcomes these limitations by operating on non-Euclidean domains and by capturing the spatial, topological, and physicochemical features that govern protein function.

This perspective provides a comprehensive and critical overview of GDL applications in stability prediction, functional annotation, molecular interaction modeling, and de novo protein design. It consolidates methodological principles, architectural diversity, and performance trends across representative studies, emphasizing how GDL enhances interpretability and generalization in protein science. Aimed at both computational method developers and experimental protein engineers, the review bridges algorithmic concepts with practical design considerations, offering guidance on data representation, model selection, and evaluation strategies.

By integrating explainable artificial intelligence and structure-based validation within a unified conceptual framework, this work highlights how GDL can serve as a foundation for transparent, interpretable, and autonomous protein design. As GDL converges with generative modeling, molecular simulation, and high-throughput experimentation, it is poised to become a cornerstone technology for next-generation protein engineering and synthetic biology.

通过将几何深度学习（GDL）集成到计算设计工作流程中，蛋白质工程正在经历一场范式转型。虽然理性设计和定向进化等传统方法已经取得了重大进展，但它们仍然受到序列空间浩瀚和实验验证成本的限制。GDL通过在非欧几里得结构域上操作和捕获控制蛋白质功能的空间、拓扑和物理化学特征来克服这些限制。这一观点提供了GDL在稳定性预测、功能注释、分子相互作用建模和从头蛋白质设计方面应用的全面和关键概述。它整合了代表性研究的方法论原则、架构多样性和性能趋势，强调GDL如何增强蛋白质科学的可解释性和泛化性。针对计算方法开发人员和实验蛋白质工程师，该综述将算法概念与实际设计考虑联系起来，为数据表示，模型选择和评估策略提供指导。通过在统一的概念框架内集成可解释的人工智能和基于结构的验证，这项工作突出了GDL如何作为透明、可解释和自主的蛋白质设计的基础。随着GDL与生成建模、分子模拟和高通量实验的融合，它将成为下一代蛋白质工程和合成生物学的基石技术。

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引用次数: 0

Enzyme symphony in bio-inspired multi-enzyme cascades for enhanced biosynthesis 酶交响乐在生物启发的多酶级联，以增强生物合成

IF 12.5 1区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biotechnology advances

Pub Date : 2025-12-26 DOI: 10.1016/j.biotechadv.2025.108789

Huan Liu , Lunjie Wu , Songyin Zhao , Yan Xu , Yao Nie

Artificial multi-enzyme cascades utilize enzymatic catalysis to achieve continuous complex biosynthesis, thus, standing out as a promising approach. The remarkable efficiency of cascade reactions arises from the precise coordination among multiple enzymes, which facilitates efficient intermediate transfer and maximizing pathway flux. This coordination closely parallels the precise and synchronized collaboration of performers in a symphony orchestra. This review summarizes the value-added biosynthetic capabilities of multi-enzyme cascades, focusing on their ability to convert inexpensive substrates into high-value complex products. Various working forms of multi-enzyme systems are presented and primarily classified into four approaches: free enzymes, assembled complexes, fusion enzymes, and bio-based immobilized enzymes, with emphasis on the unique value of confined microenvironments as crucial platforms for achieving highly efficient cascade reactions. Furthermore, we emphasize the spatial architecture and dynamic regulation of multi-enzyme complexes, while exploring strategies for the rational design of artificial multi-enzyme assemblies tailored to cascade reaction requirements. Finally, emerging trends in AI-assisted design of cascade reactions and multi-enzyme complexes are highlighted to guide future developments.

人工多酶级联利用酶催化实现连续复杂的生物合成，因此是一种很有前途的方法。级联反应的高效源于多种酶之间的精确协调，从而促进了高效的中间体传递和最大化途径通量。这种协调与交响乐团中表演者精确而同步的合作非常相似。本文综述了多酶级联的增值生物合成能力，重点介绍了它们将廉价底物转化为高价值复杂产品的能力。介绍了多酶系统的各种工作形式，主要分为四种方法：自由酶、组装复合物、融合酶和生物基固定化酶，重点介绍了受限微环境作为实现高效级联反应的关键平台的独特价值。此外，我们强调多酶复合物的空间结构和动态调控，同时探索适合级联反应要求的人工多酶组件的合理设计策略。最后，重点介绍了人工智能辅助设计级联反应和多酶复合物的新趋势，以指导未来的发展。

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引用次数: 0

Spatial transcriptomics: integrating platforms and computational approaches for clinical insights 空间转录组学：整合平台和临床见解的计算方法

IF 12.5 1区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biotechnology advances

Pub Date : 2025-12-26 DOI: 10.1016/j.biotechadv.2025.108791

Wei Song , Duo Wang , Jinming Li , Rui Zhang

Spatial transcriptomics (ST) is a significant advancement in life science research, enabling transcriptome analysis to transition from traditional bulk and single-cell levels to spatial location levels, thereby expanding the boundaries of biological research and pathological diagnosis. This technological breakthrough has provided unprecedented insights into complex biological processes, disease mechanisms, and clinical diagnosis. Despite the impressive advances in the field in recent years, it still faces several challenges, including technical complexity, difficulties in data analysis, and the lack of standardization. This review provides a comprehensive comparison of the technical principles and data analysis processes of ST, while also summarizing its latest applications and the current state of standardization. It aims to provide researchers with a clear framework for understanding the progresses, challenges, and future directions, thereby promoting the further development and clinical transition of ST technologies.

空间转录组学（ST）是生命科学研究的重大进展，使转录组分析从传统的体积和单细胞水平过渡到空间定位水平，从而扩大了生物学研究和病理诊断的界限。这项技术突破为复杂的生物过程、疾病机制和临床诊断提供了前所未有的见解。尽管近年来该领域取得了令人印象深刻的进步，但它仍然面临着一些挑战，包括技术复杂性、数据分析困难以及缺乏标准化。本文对ST的技术原理和数据分析过程进行了全面的比较，同时总结了其最新应用和标准化的现状。旨在为研究人员了解ST技术的进展、挑战和未来方向提供一个清晰的框架，从而促进ST技术的进一步发展和临床转型。

引用次数: 0

Viral clearance in biopharmaceutical manufacturing: Current strategies, challenges, and future directions 生物制药生产中的病毒清除：当前策略、挑战和未来方向

IF 12.5 1区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biotechnology advances

Pub Date : 2025-12-23 DOI: 10.1016/j.biotechadv.2025.108784

Dhruvkumar Hariharbhai Soni , V. Reghellin , G. Sbarufatti , P. Minghetti , A. Altomare

Viral safety remains a fundamental requirement in the manufacturing of monoclonal antibodies (mAbs), particularly due to the widespread use of mammalian cell lines susceptible to both endogenous and adventitious viral contamination. This review provides a comprehensive overview of current viral clearance strategies integrated into downstream processing (DSP), highlighting the mechanisms, performance, and practical implementation of key unit operations. Chromatographic methods, including Protein A affinity, ion exchange (CEX and AEX), hydrophobic interaction (HIC), and mixed-mode chromatography (MMC), contribute to virus removal to varying extents, depending on virus type, resin chemistry, and process conditions. Anion exchange membranes have demonstrated high log reduction values (LRVs), especially for small non-enveloped viruses, while mixed-mode resins enhance removal through dual-mode interactions. Dedicated viral inactivation steps, such as low-pH incubation and detergent treatment, remain effective against enveloped viruses, with the use of stabilizing agents like arginine and extremolytes increasingly adopted to preserve product quality. Virus filtration continues to represent the most robust barrier to small viruses, though its performance depends on parameters such as filter material, fouling tendency, and viral load. Emerging solutions, such as activated carbon filtration and membrane chromatography, offer scalable, orthogonal alternatives compatible with disposable and continuous processing formats. Notably, viral clearance strategies have been successfully incorporated into continuous downstream workflows, including multicolumn capture, inline inactivation, and extended-duration filtration. Collectively, these advances support the transition toward more flexible, efficient, and sustainable viral safety frameworks, paving the way for next-generation biomanufacturing platforms.

病毒安全性仍然是制造单克隆抗体（mab）的基本要求，特别是由于广泛使用对内源性和外源性病毒污染敏感的哺乳动物细胞系。这篇综述提供了当前整合到下游处理（DSP）的病毒清除策略的全面概述，重点介绍了关键单元操作的机制、性能和实际实施。层析方法，包括蛋白A亲和、离子交换（CEX和AEX）、疏水相互作用（HIC）和混合模式层析（MMC），根据病毒类型、树脂化学和工艺条件的不同，在不同程度上有助于去除病毒。阴离子交换膜具有很高的对数还原值（lrv），特别是对于小的非包膜病毒，而混合模式树脂通过双模式相互作用增强了去除效果。专门的病毒灭活步骤，如低ph孵育和洗涤剂处理，对包膜病毒仍然有效，使用稳定剂，如精氨酸和极溶物，以保持产品质量越来越多地被采用。病毒过滤仍然是对小病毒最强大的屏障，尽管其性能取决于过滤材料、污垢趋势和病毒载量等参数。新兴的解决方案，如活性炭过滤和膜色谱，提供可扩展的、正交的替代方案，与一次性和连续处理格式兼容。值得注意的是，病毒清除策略已成功地整合到连续的下游工作流程中，包括多柱捕获、内嵌灭活和长时间过滤。总的来说，这些进展支持向更灵活、高效和可持续的病毒安全框架过渡，为下一代生物制造平台铺平了道路。

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引用次数: 0

UGT708 glycosyltransferases: Nature's architects of C-glycosides UGT708糖基转移酶：自然界c -糖苷的建筑师

IF 12.5 1区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biotechnology advances

Pub Date : 2025-12-22 DOI: 10.1016/j.biotechadv.2025.108787

Bhawna Verma , Palak Arora , Shahnawaz Hussain , Ritu Devi , Suphla Gupta

Uridine Diphosphate glycosyltransferases (UGTs) catalyze the transfer of glycosyl moieties from donors to acceptors, a modification critical for plant growth, development, and metabolic homeostasis. These enzymes are ubiquitous across all life domains, playing key roles in the biosynthesis of diverse glycosides. This review focuses on the plant UGT708 family, which is uniquely characterized by its ability to form stable C-glycosidic bonds on flavonoid backbones, enhancing metabolic stability and bioactivity. We conducted a comprehensive analysis of UGT distribution and functional evolution across life forms, highlighting their evolutionary significance and diversification. Emphasizing plant-specific adaptations, UGT708 enzymes specialize in C-glycosylation of flavonoid, polyphenolic compounds, diketones and aromatic hydrocarbons, particularly 2-hydroxyflavanones and polyhydroxy ketones, facilitating the production of defense metabolites such as schaftosides and isoschaftosides which enhance plant resilience to environmental stresses. Clade-specific variations in the conserved PSPG motif, notably in monocots, correlate with differences in sugar-donor specificity and substrate promiscuity, reflecting structural and functional diversity within the family. Structural analyses reveal key active site residues responsible for selective C-glycosylation, resulting in a broad spectrum of glycosides with ecological and therapeutic importance. While UGT708s hold great promise as biocatalysts for generating bioactive compounds, more detailed studies on their molecular interactions and catalytic mechanisms are essential to fully exploit their potential in agriculture, medicine, and industry.

尿苷二磷酸糖基转移酶（UGTs）催化糖基从供体向受体的转移，这是一种对植物生长、发育和代谢稳态至关重要的修饰。这些酶普遍存在于所有生命领域，在多种糖苷的生物合成中起着关键作用。本文综述了植物UGT708家族，其独特的特点是能够在类黄酮骨架上形成稳定的c -糖苷键，增强代谢稳定性和生物活性。我们对不同生命形式的UGT分布和功能演化进行了综合分析，强调了UGT的进化意义和多样性。UGT708酶强调植物的特异性适应性，专注于类黄酮、多酚化合物、二酮和芳烃的c -糖基化，特别是2-羟基黄酮和多羟基酮，促进防御代谢物的产生，如schaftosides和异schaftosides，增强植物对环境胁迫的适应能力。保守的PSPG基序的枝特异性变异，特别是在单子房中，与糖供体特异性和底物乱交的差异相关，反映了家族内结构和功能的多样性。结构分析揭示了负责选择性c -糖基化的关键活性位点残基，从而产生具有生态和治疗重要性的广谱糖苷。虽然UGT708s作为产生生物活性化合物的生物催化剂具有很大的前景，但对其分子相互作用和催化机制的更详细研究对于充分利用其在农业、医学和工业中的潜力至关重要。

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引用次数: 0

From traditional to AI-driven: The evolution of intelligent enzyme engineering for biocatalysis 从传统到人工智能驱动：生物催化智能酶工程的演变

IF 12.5 1区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biotechnology advances

Pub Date : 2025-12-22 DOI: 10.1016/j.biotechadv.2025.108788

Feifei Lv , Jiaxing Zhang , Shengping You , Wei Qi

Enzyme engineering involves enhancing enzyme function and application through multidimensional technological systems. Its development encompasses elucidating sequence-structure-function relationships, exploring fitness landscapes, and multiscale regulation. Conventional enzyme engineering strategies include directed evolution (DE), rational/semi-rational design, residue co-evolution, and de novo design. DE mimics natural selection but is limited by high-throughput screening efficiency. Rational/semi-rational design integrates computational simulation with experimental validation to regulate enzyme performance. Residue co-evolution combines sequence co-evolution analysis and kinetic simulations, and de novo design is dedicated to achieving precise protein folding through physical modeling. In recent years, the breakthrough progress in machine learning (ML), especially deep learning (DL), has significantly enhanced the efficiency of all the above-mentioned methods; by accurately predicting mutational effects and efficiently exploring discontinuous sequence space, it provides powerful tools to improve or supplement the above strategies, thereby aiding in escaping local fitness optima traps. This review summarizes common strategies in enzyme engineering, including directed evolution, rational/semi-rational design, residue co-evolution, and de novo design, and further introduces the latest applications of ML and DL models in each of these fields. Although challenges persist, such as force field accuracy limitations, mutation sampling constraints, experimental throughput limitations, and epistatic effects, more comprehensive multimodal foundation models in the future are expected to integrate cross-scale parameters for intelligent design, and the establishment of standardized enzymology databases will enhance prediction reliability. Overall, AI-empowered enzyme engineering will drive a profound transformation toward a predictable and highly efficient pathway for enzyme design, providing more precise and powerful solutions for biocatalysis.

酶工程是通过多维技术体系来增强酶的功能和应用。它的发展包括阐明序列-结构-功能关系，探索适应度景观和多尺度调节。传统的酶工程策略包括定向进化（DE）、理性/半理性设计、残基协同进化和从头设计。DE模拟自然选择，但受限于高通量筛选效率。理性/半理性设计将计算模拟与实验验证相结合来调节酶的性能。残基协同进化将序列协同进化分析与动力学模拟相结合，de novo设计致力于通过物理建模实现精确的蛋白质折叠。近年来，机器学习（ML）特别是深度学习（DL）的突破性进展显著提高了上述所有方法的效率；通过准确预测突变效应和有效地探索不连续序列空间，为改进或补充上述策略提供了强大的工具，从而帮助摆脱局部适应度最优陷阱。本文综述了酶工程的常用策略，包括定向进化、理性/半理性设计、残基协同进化和从头设计，并进一步介绍了ML和DL模型在这些领域的最新应用。尽管存在力场精度限制、突变采样约束、实验通量限制和上位效应等挑战，但未来更全面的多模态基础模型有望整合跨尺度参数进行智能设计，标准化酶学数据库的建立将提高预测的可靠性。总的来说，人工智能酶工程将推动酶设计向可预测和高效的途径的深刻转变，为生物催化提供更精确和强大的解决方案。

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引用次数: 0

Membranes: The only chance for the mRNA tortoise to win, economically 提交给《生物技术进展》的主要手稿题为：观点：“膜：mRNA龟在经济上获胜的唯一机会”

IF 12.5 1区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biotechnology advances

Pub Date : 2025-12-21 DOI: 10.1016/j.biotechadv.2025.108774

Thomas G. Neuman , Surya Karla , James (Chip) Kilduff , Joel Plawsky , Georges Belfort

The RNA revolution, advancing beyond traditional vaccines to new therapeutic modalities and constructs such as self-amplifying RNA (saRNA) and circular RNA (circRNA), continues to place increasing pressure on downstream purification. Diffusion-limited resins, the time-tested workhorse of protein purification, are fundamentally incompatible because mRNA is an enormous (>40 nm) molecule with low diffusivity (10⁻¹¹–10⁻¹² m²/s). Our perspective, rooted in core chemical engineering principles, applies transport analysis and re-examines published performance data to demonstrate why even optimized perfusion chromatographic resin systems, exhibiting 0.1 % of total flow through the resin particles, cannot overcome the inherent diffusional barriers preventing efficient RNA purification. Alternatively, convection-based devices, notably membranes and monoliths, are well situated as their transport characteristics are not limited by the molecular transport properties of RNA. Ultimately, pressure-driven flow enables the potential for increased device capacity at orders of magnitude (10³x) lower process time and smaller device footprint contributing to markedly improved productivity. Taken together, these findings suggest that a paradigm shift is required toward convective membrane systems to create a platform capable of delivering scalable, economic, and ultimately industrially attractive mRNA purification.

RNA革命从传统疫苗发展到新的治疗方式和结构，如自我扩增RNA （saRNA）和环状RNA (circRNA)，这继续给下游纯化带来越来越大的压力。由于mRNA是一个巨大的（>40 nm）分子，具有低扩散率（10−11-10−12 m2/s），因此限制扩散的树脂是久经考验的蛋白质纯化的主要工具，从根本上来说是不相容的。我们的观点根植于核心化学工程原理，应用输运分析并重新检查已发表的性能数据，以证明为什么即使优化的灌注色谱树脂系统，显示通过树脂颗粒的总流量为0.1% %，也不能克服阻碍有效RNA纯化的固有扩散障碍。另外，基于对流的装置，特别是膜和单体，由于其运输特性不受RNA分子运输特性的限制而处于良好的位置。最终，压力驱动的流体能够以数量级（103x）的速度增加设备容量，缩短工艺时间，减小设备占地面积，从而显著提高生产率。综上所述，这些发现表明，需要对对流膜系统进行范式转变，以创建一个能够提供可扩展、经济且最终具有工业吸引力的mRNA纯化的平台。

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引用次数: 0

The advances of strategies and technologies in high-throughput microbial culturomics 高通量微生物培养策略与技术进展

IF 12.5 1区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biotechnology advances

Pub Date : 2025-12-20 DOI: 10.1016/j.biotechadv.2025.108783

Zheng-Hui Li , Shuang Li , Hai-Bo Chen , Yi-Zhi Ji , Chong Zhang

The growing demand for novel antibiotics, industrial enzymes, and environmentally sustainable biotechnological solutions is drawing increasing attention to more efficient strategies for microbial resource discovery across biomedical, industrial, and ecological domains. To meet this need, high-throughput microbial culturomics has emerged as a powerful strategy that integrates advanced cultivation platforms, diverse growth conditions, and rapid identification technologies. This review explores two principal paradigms within high-throughput microbial culturomics: the function-driven screening-first strategy and the enrichment-based cultivation-first strategy. Their technical foundations, application scenarios, and inherent limitations are examined in detail, providing a comparative analysis of their respective advantages and challenges. We further emphasize the importance of high-throughput identification methods, which play a crucial role in classifying isolates and revealing their functional potential. Ultimately, the review explores future directions for the development of automated, fully integrated cultivation platforms that integrate large-scale experimentation with data-driven optimization. By offering a structured comparison of culturomics strategies, integration pathways, and key obstacles, this review serves as a methodological reference for advancing microbial isolation, functional screening, and biotechnological innovation.

对新型抗生素、工业酶和环境可持续生物技术解决方案的需求日益增长，越来越多的人关注在生物医学、工业和生态领域发现更有效的微生物资源策略。为了满足这一需求，高通量微生物培养组学已经成为一种强大的策略，它集成了先进的培养平台、多样化的生长条件和快速鉴定技术。本文综述了高通量微生物培养组学的两个主要范式：功能驱动的筛选优先策略和基于富集的培养优先策略。详细分析了它们的技术基础、应用场景和固有局限性，并对各自的优势和挑战进行了比较分析。我们进一步强调高通量鉴定方法的重要性，它在分离物分类和揭示其功能潜力方面起着至关重要的作用。最后，本文探讨了自动化、全集成栽培平台的未来发展方向，该平台将大规模实验与数据驱动优化相结合。通过对培养组学策略、整合途径和关键障碍进行结构化比较，本综述可为推进微生物分离、功能筛选和生物技术创新提供方法学参考。

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引用次数: 0

Synthetic biology strategies for engineering probiotics and commensal bacteria for diagnostics and therapeutics 用于诊断和治疗的工程益生菌和共生菌的合成生物学策略

IF 12.5 1区工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Biotechnology advances

Pub Date : 2025-12-18 DOI: 10.1016/j.biotechadv.2025.108782

Jiwoo Nam , Yuna Lee , Sion Lee , Hyungjun Choi , Sang Yup Lee , Dongsoo Yang

Microorganisms inhabit diverse environments, including nearly every organ in the human body. The human microbiome—a complex community of microorganisms residing in the human body—has gained increasing attention as a key contributor to human health and disease, making it an important target for the development of diagnostic and therapeutic strategies. However, the inherent complexity of microbial communities and the challenges of engineering diverse non-model microorganisms present significant barriers. To address these challenges, synthetic biology has provided powerful tools and strategies to engineer microorganisms capable of sensing disease-specific environments and performing targeted therapeutic functions. In particular, the development of synthetic genetic circuits has significantly improved the precision and reliability of disease diagnosis and treatment, enabling real-time disease monitoring, therapeutic, and even preventive interventions. This review highlights state-of-the-art synthetic biology tools and strategies for engineering the probiotics and commensal bacteria aimed at the diagnosis and treatment of human diseases, with accompanying examples. Future challenges and prospects are also discussed.

微生物生活在各种各样的环境中，几乎包括人体的每个器官。人类微生物群是居住在人体内的复杂微生物群落，作为人类健康和疾病的关键贡献者而受到越来越多的关注，使其成为诊断和治疗策略发展的重要目标。然而，微生物群落的固有复杂性和工程多样化非模式微生物的挑战提出了重大障碍。为了应对这些挑战，合成生物学提供了强大的工具和策略来设计能够感知疾病特定环境并执行靶向治疗功能的微生物。特别是，合成基因电路的发展显著提高了疾病诊断和治疗的准确性和可靠性，使实时疾病监测、治疗甚至预防干预成为可能。本综述重点介绍了用于诊断和治疗人类疾病的益生菌和共生菌工程的最先进的合成生物学工具和策略，并附有示例。讨论了未来的挑战和展望。

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引用次数: 0