Advanced Agrochem最新文献

Dual-strategy approach for fungicide discovery: Machine learning-based activity prediction and fragment co-occurrence network construction 杀菌剂发现的双策略方法：基于机器学习的活性预测和片段共现网络构建

Advanced Agrochem

Pub Date : 2025-12-01 DOI: 10.1016/j.aac.2025.10.005

Binyan Jin, Jialin Cui, Qi He, Huan Xu, Xinpeng Sun, Ziyao Chai, Li Zhang

The development of fungicides is time-consuming and costly. Introducing a fungicide-likeness assessment strategy at the early screening stage can help reduce development risks and improve the success rate. However, existing assessment methods are often plagued by low accuracy and poor generalization, while fragment-based design strategies commonly fail to account for synergistic effects between structural units. Therefore, based on a small-scale sample set, this study developed a more efficient global predictive model for fungicidal activity—named APPf—by integrating multi-scale feature screening methods and machine learning algorithms, which also accounts for synergistic effects among different structural fragments. We utilized three independent external test sets for model validation: External Test Set 1 for general validation, External Test Set 2 for comparison with existing models, and External Test Set 3 for disease-specific fungicide evaluation. On External Test Set 1, the APPf model achieved a precision of 0.6454, a recall of 0.8535, and an F1 score of 0.7350, demonstrating its robust predictive performance. It also exhibited strong enrichment capability for positive samples in External Test Set 2. For External Test Set 3, APPf achieved a prediction accuracy exceeding 80% for each disease, suggesting its promising potential in practical fungicide development. Furthermore, we quantified the contribution of molecular descriptors to the model predictions using SHAP value analysis and identified nHdNH and NssssNp as strong indicative features for predicting fungicidal activity, thereby enhancing the interpretability of the model. APPf has been deployed on a public web server (http://pesticides.cau.edu.cn/APPf), providing a user-friendly online prediction service to support the discovery of novel fungicides. Meanwhile, we employed a molecular fragmentation strategy to analyze the co-occurrence relationships between fragments in fungicides and constructed a network map of fragment co-occurrence associated with fungicidal activity. This study provides both an active fragment library and a global fungicide-likeness assessment tool for AI-based de novo molecular generation aimed at discovering novel fungicidal leads, which is expected to enhance the efficiency of developing new fungicides.

杀菌剂的开发既耗时又昂贵。在早期筛查阶段引入杀菌剂相似度评估策略有助于降低发展风险，提高成功率。然而，现有的评估方法往往存在精度低、泛化差的问题，而基于碎片的设计策略往往无法考虑结构单元之间的协同效应。因此，本研究基于小尺度样本集，结合多尺度特征筛选方法和机器学习算法，在考虑不同结构片段之间协同效应的基础上，建立了更高效的杀菌剂活性全局预测模型——appf。我们使用三个独立的外部测试集进行模型验证：外部测试集1用于一般验证，外部测试集2用于与现有模型进行比较，外部测试集3用于疾病特异性杀菌剂评估。在External Test Set 1上，APPf模型的准确率为0.6454，召回率为0.8535，F1分数为0.7350，显示了其稳健的预测性能。在外部测试集2中对阳性样品也表现出较强的富集能力。在外部测试集3中，APPf对每种疾病的预测准确率均超过80%，表明其在杀菌剂的实际开发中具有很大的潜力。此外，我们使用SHAP值分析量化了分子描述符对模型预测的贡献，并确定了nHdNH和NssssNp是预测杀真菌活性的强指示性特征，从而提高了模型的可解释性。APPf已部署在一个公共web服务器（http://pesticides.cau.edu.cn/APPf）上，提供一个用户友好的在线预测服务，以支持发现新的杀菌剂。同时，我们采用分子片段化策略分析了杀菌剂中片段共现关系，构建了片段共现与杀菌剂活性相关的网络图谱。该研究为基于人工智能的从头分子生成提供了活性片段库和全局杀菌剂相似性评估工具，旨在发现新的杀菌剂先导物，有望提高开发新的杀菌剂的效率。

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引用次数: 0

Sensing the dry: Redox-regulated auxin repression shapes root plasticity 感知干燥：氧化还原调节的生长素抑制形成根的可塑性

Advanced Agrochem

Pub Date : 2025-12-01 DOI: 10.1016/j.aac.2025.10.006

Ali Shahzad , Hameed Gul

Root architecture is crucial for plant survival under drought, yet how water stress signals shape root development has remained unclear. Recently, Roy et al. (Science) revealed that drought-induced accumulation of reactive oxygen species (ROS) in root nuclei triggers the auxin repressor IAA3 to form complexes (multimerization) that enable the recruitment of the co-repressor TOPLESS (TPL), thereby suppressing auxin signaling and blocking lateral root initiation. ROS signaling acts rapidly, within minutes of water deprivation, compared to slower ABA responses, allowing plants to quickly adjust lateral root growth. This discovery reveals a new redox–hormone regulatory mechanism that enables plants to fine-tune root branching during transient drought. Beyond advancing our understanding of adaptive root responses, these insights highlight potential molecular targets for developing crops with improved resilience under water limitation.

根系结构对植物在干旱条件下的生存至关重要，但水分胁迫信号如何影响根系发育尚不清楚。最近，Roy等人（Science）发现，干旱诱导的根核活性氧（ROS）的积累会触发生长素抑制因子IAA3形成复合物（多聚化），从而使协同抑制因子toppless （TPL）得以招募，从而抑制生长素信号传导并阻断侧根形成。与ABA反应较慢相比，ROS信号在缺水几分钟内就能迅速发挥作用，使植物能够迅速调整侧根生长。这一发现揭示了一种新的氧化还原激素调节机制，使植物能够在短暂干旱期间微调根分枝。除了促进我们对适应性根系反应的理解之外，这些见解还强调了在水分限制下开发具有更好抗逆性的作物的潜在分子靶标。

引用次数: 0

Aflatoxin B1 contamination level detection in almond kernels through short wave infrared hyperspectral image analysis 利用短波红外高光谱图像分析检测杏仁中黄曲霉毒素B1的污染水平

Advanced Agrochem

Pub Date : 2025-12-01 DOI: 10.1016/j.aac.2025.03.006

Md. Ahasan Kabir , Ivan Lee , Chandra B. Singh , Gayatri Mishra , Brajesh Kumar Panda , Sang-Heon Lee

Aflatoxin B1 (AFB1) is a toxic fungal metabolite that contaminates almonds from cultivation to harvesting. It leads to chronic health problems and significant economic loss to the producers. Therefore, a fast and non-invasive detection technique is crucial for safeguarding food safety by swiftly identifying and eliminating contaminated almonds from the supply chain. Hyperspectral imaging has been explored as a potential non-destructive technology for detecting AFB1. However, the diverse geometries of almonds present a significant challenge on acquired images, thereby impacting the accuracy of the developed prediction and classification models. This study investigates the effectiveness of short-wave infrared (SWIR) hyperspectral imaging combined with deep learning for detecting AFB1 in almonds of varying geometries. Initially, partial least squares regression (PLSR) and support vector machine (SVM) regression models were evaluated for quantification, while SVM and quadratic discriminant analysis (QDA) classifiers were applied for classification. The results indicated that spectral responses varied with almond thickness, making quantification models unreliable for industrial applications. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was employed to identify key spectral features for developing multi-spectral AFB1 classification models to evaluate the feasibility of high-speed, accurate in-line detection. The deep learning approach significantly outperformed traditional machine learning models, with the pre-trained Inception V3 network achieving a cross-validation accuracy of 84.82 %, an F1-score of 0.8522, and an area under curve of 0.893. These findings highlight the superiority of deep learning-based hyperspectral imaging for accurate and reliable AFB1 detection in almonds with diverse shapes and thicknesses.

黄曲霉毒素B1 （AFB1）是一种有毒的真菌代谢物，从种植到收获都会污染杏仁。它给生产者造成慢性健康问题和重大经济损失。因此，通过快速识别和消除供应链中受污染的杏仁，一种快速且非侵入性的检测技术对于保障食品安全至关重要。高光谱成像作为一种潜在的非破坏性检测AFB1技术已被探索。然而，杏仁的不同几何形状对获取的图像提出了重大挑战，从而影响了所开发的预测和分类模型的准确性。本研究探讨了短波红外（SWIR）高光谱成像结合深度学习检测不同几何形状杏仁中AFB1的有效性。首先，对偏最小二乘回归（PLSR）和支持向量机（SVM）回归模型进行量化评估，并采用SVM和二次判别分析（QDA）分类器进行分类。结果表明，光谱响应随杏仁厚度的变化而变化，使得量化模型在工业应用中不可靠。采用竞争自适应重加权采样（CARS）算法识别关键光谱特征，建立多光谱AFB1分类模型，以评估高速、准确在线检测的可行性。深度学习方法显著优于传统机器学习模型，预训练的Inception V3网络交叉验证准确率为84.82%，f1得分为0.8522，曲线下面积为0.893。这些发现突出了基于深度学习的高光谱成像在不同形状和厚度的杏仁中准确可靠地检测AFB1的优势。

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引用次数: 0

Heuristic ab initio elucidation of low-level impurity structures in clothianidin material 启发式从头算分析噻虫胺材料中低杂质结构

Advanced Agrochem

Pub Date : 2025-12-01 DOI: 10.1016/j.aac.2025.03.004

Mengling Tu , Wen Ma , Yunxiao Zhu , Yang Liu , Xianjiang Li

Clothianidin (CLO) is an insecticide with a high prevalence in environment and food samples. The identification of structural impurities is of great importance for the development of certified reference materials. Here, a heuristic method for CLO impurity analysis combining liquid chromatography-high resolution mass spectrometry (Orbitrap) and a molecular annotation platform (SIRIUS) was applied. Precursor and product ion mass data was used to predict candidate chemical formulas, and SIRIUS, isotopes, fragmentation trees and ZODIAC scores were calculated for ranking. The chemical structures of the impurities were inferred based on the characteristic fragments of the main component CLO. Finally, 25 impurities were identified and classified into four groups based on their structural differences. Among them, 3 impurities had CAS registration numbers and 1 impurity was validated with a standard by HPLC-UV and mass spectrum. This work successfully combines ab initio identification tools with intellect in the analysis of structural related impurities.

噻虫胺（CLO）是一种在环境和食品中普遍存在的杀虫剂。结构杂质的鉴定对标准物质的开发具有重要意义。本研究采用液相色谱-高分辨率质谱（Orbitrap）和分子注释平台（SIRIUS）相结合的启发式方法进行CLO杂质分析。前驱体和产物离子质量数据预测候选化学式，并计算天狼星、同位素、碎片树和ZODIAC评分进行排序。根据主要成分CLO的特征片段推断杂质的化学结构。最后，根据结构差异，鉴定出25种杂质，并将其分为4类。其中3个杂质具有CAS注册号，1个杂质经HPLC-UV和质谱验证。这项工作成功地将从头算鉴定工具与智能结合在结构相关杂质的分析中。

引用次数: 0

Discovery of diphenyl ethers as novel inhibitors of insect trehalase via virtual screening and biological assays 通过虚拟筛选和生物试验发现二苯醚作为昆虫海藻酶的新型抑制剂

Advanced Agrochem

Pub Date : 2025-12-01 DOI: 10.1016/j.aac.2024.12.001

Xi Jiang , Wenda Li , Qiong Lu , Yi Ding , Mingjia Gao , Shanru He , Wei Liu , Yong Zhou , Tian Liu

Trehalase hydrolyzes trehalose to glucose to provide energy for insects or building blocks for chitin synthesis. Because trehalase is critical to insects but not to humans, it has long been considered a promising target for green insecticides. However, the known trehalase inhibitors are mainly sugar derivatives with poor druggability. In this study, the trehalase from Ostrinia furnacalis (OfTreh) was expressed and characterized. By integrative computational strategies, diphenyl ether herbicides were discovered as the first non-carbohydrate inhibitors of insect trehalases. Bifenox and its more stable derivative, chlomethoxyfen, inhibited OfTreh with K_i values of 56 and 43 μM, respectively. The oral administration of bifenox or chlomethoxyfen to locusts resulted in the inhibition of trehalose hydrolysis in vivo, leading to a mortality rate of 66 % and server locomotion disorder in the survivors. This study not only established a platform for the development of insecticides targeting trehalase but also discovered a new mechanism for diphenyl ethers to kill insects as trehalase inhibitors.

海藻糖酶将海藻糖水解为葡萄糖，为昆虫提供能量或为几丁质合成提供原料。由于海藻酶对昆虫至关重要，但对人类并不重要，因此它一直被认为是绿色杀虫剂的一个有希望的目标。然而，已知的海藻糖酶抑制剂主要是糖衍生物，药物性较差。本研究对Ostrinia furnacalis （OfTreh）海藻化酶进行了表达和表征。通过综合计算策略，二苯基醚除草剂被发现是昆虫海藻酶的第一个非碳水化合物抑制剂。联苯醚及其更稳定的衍生物氯甲氧芬对OfTreh的抑制作用Ki值分别为56 μM和43 μM。口服联苯醚或氯甲氧芬可抑制蝗虫体内海藻糖水解，导致幸存者死亡率达66%，运动障碍。本研究不仅为海藻糖酶靶向杀虫剂的开发提供了平台，而且还发现了二苯醚作为海藻糖酶抑制剂杀死昆虫的新机制。

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引用次数: 0

Targeted stabilization of MYC2 protein: AI-driven resistance design conquers citrus Huanglongbing MYC2蛋白靶向稳定：ai驱动抗性设计征服柑橘黄龙冰

Advanced Agrochem

Pub Date : 2025-12-01 DOI: 10.1016/j.aac.2025.09.003

Ziyue Liu, Yifei Li, Hongchen Liu, Yiting Pu, Jiaxin Tang, Siyuan Feng, Qiyang Min, Kun Qian

This Highlight discusses the landmark study by Zhao et al. (Science, 2025) that presents a transformative strategy against citrus Huanglongbing (HLB). The work identifies the E3 ubiquitin ligase PUB21 as a central susceptibility (S) factor, degrading the defense regulator MYC2. Crucially, the study harnesses natural resistance (dominant-negative PUB21DN mutant) and pioneers AI-driven design to develop a 14-amino acid peptide (APP3-14). This peptide dually combats HLB by stabilizing MYC2 (inhibiting PUB21) and directly targeting the unculturable pathogen Candidatus Liberibacter asiaticus (CLas), achieving >90 % bacterial reduction in field trials. The research also exposes how a CLas effector (SDE5, Sec-delivered effector 5) hijacks the PUB21-MYC2 axis. This work establishes “defense protein stabilization” as a powerful new paradigm for breeding resistant crops and controlling recalcitrant pathogens, exemplified by the innovative integration of AI in peptide therapeutics for plants.

本专题讨论了Zhao等人（Science, 2025）提出的针对柑橘黄龙冰（HLB）的转化策略的里程碑式研究。这项工作确定E3泛素连接酶PUB21是一个中心易感性(S)因子，降低防御调节因子MYC2。至关重要的是，该研究利用了自然抗性（显性阴性PUB21DN突变体），并开创了人工智能驱动设计，开发了一种14个氨基酸的肽（APP3-14）。该肽通过稳定MYC2（抑制PUB21）和直接靶向不可培养的asiaticcandidatus Liberibacter （CLas）双重对抗HLB，在田间试验中实现了90%的细菌减少。该研究还揭示了CLas效应（SDE5， sec传递的效应5）如何劫持PUB21-MYC2轴。这项工作建立了“防御蛋白稳定”作为培育抗性作物和控制顽固性病原体的强大新范例，以人工智能在植物肽治疗中的创新整合为例。

引用次数: 0

In-silico analysis of the mechanism of action of Nerium oleander bioactive compounds against Helicoverpa armigera 夹竹桃生物活性化合物对棉铃虫的作用机理的计算机分析

Advanced Agrochem

Pub Date : 2025-12-01 DOI: 10.1016/j.aac.2025.09.001

Karthika Sunil , Tinu Thomas

Helicoverpa armigera is one of the most destructive agricultural pests worldwide, noted for its wide host range, high fecundity, and rapid development of resistance to synthetic insecticides. To address this threat, sustainable botanical alternatives are urgently needed. In this study, Nerium oleander, a toxic ornamental plant rich in secondary metabolites, was evaluated as a potential botanical insecticide through in silico assays. Methanolic extracts were subjected to phytochemical screening, confirming the presence of alkaloids, saponins, cardiac glycosides, coumarins, and terpenoids. Gas Chromatography-Mass Spectrometry (GC-MS) profiling identified 20 major compounds, including terpenoids, fatty acids, sterols, and phenolics, with 2-methoxy-4-vinylphenol (2.7 %), neophytadiene (1.7 %), and phytol (0.9 %) among the key constituents. Cytochrome P450, a central detoxification enzyme in insects, was chosen as the molecular target. Docking analysis revealed strong binding affinities, with phytol (−6.92 kcal/mol, Ki 8.12 μM), neophytadiene (−6.43 kcal/mol, Ki 14.57 μM), and 2-methoxy-4-vinylphenol (−5.87 kcal/mol, Ki 45.13 μM) demonstrating significant inhibitory potential. These findings indicate that N. oleander metabolites may disrupt detoxification pathways in H. armigera, providing a mechanistic basis for their insecticidal action and supporting the plant's promise as a candidate for integrated pest management.

棉铃虫是世界范围内最具破坏性的农业害虫之一，具有寄主范围广、繁殖力强、对合成杀虫剂产生抗药性快等特点。为了解决这一威胁，迫切需要可持续的植物替代品。本研究以夹竹桃为研究对象，对夹竹桃作为一种富含次生代谢物的有毒观赏植物进行了有机硅分析。甲醇提取物进行植物化学筛选，确认存在生物碱、皂苷、心糖苷、香豆素和萜类。气相色谱-质谱（GC-MS）分析鉴定了20种主要化合物，包括萜类、脂肪酸、甾醇和酚类化合物，其中2-甲氧基-4-乙烯基苯酚（2.7%）、新植物二烯（1.7%）和叶绿醇（0.9%）是主要成分。选择昆虫中心解毒酶细胞色素P450作为分子靶点。对接分析显示，叶绿醇（- 6.92 kcal/mol, Ki 8.12 μM）、新叶绿二烯（- 6.43 kcal/mol, Ki 14.57 μM）和2-甲氧基-4-乙烯基酚（- 5.87 kcal/mol, Ki 45.13 μM）具有较强的抑制潜力。这些发现表明夹竹桃代谢物可能会破坏棉铃虫的解毒途径，为其杀虫作用提供了机制基础，并支持该植物作为害虫综合治理的候选植物的前景。

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引用次数: 0

Developing RNAi pesticides in the post genomic era: A review 后基因组时代RNAi农药研究进展

Advanced Agrochem

Pub Date : 2025-12-01 DOI: 10.1016/j.aac.2025.07.002

Haonan Duanmu , Fangyuan Guo , Ying Liu , Kang He

RNA interference (RNAi) has emerged as a promising platform for next-generation pesticides, offering high target specificity and environmental safety. In the post-genomic era, rapid advances in insect genomics, delivery systems, and bioinformatics have significantly accelerated the development of RNAi-based pest control strategies. This review summarizes current progress in RNAi mechanism elucidation, target gene selection, and delivery approaches including host-induced gene silencing (HIGS), spray-induced gene silencing (SIGS), and virus-induced gene silencing (VIGS). Key challenges—such as interspecies variability in RNAi efficiency, dsRNA degradation, and off-target effects—are discussed in detail. Machine learning (ML) and genome-wide screening play a critical role in optimizing siRNA design and reducing ecological risks. Commercial products such as Ledprona and MON87411 maize exemplify the practical potential of RNAi-based pesticides. Future success will depend on integrating comparative genomics, ML-based off-target prediction, and ecological risk assessment frameworks to ensure safety and sustainability in applications.

RNA干扰（RNA interference, RNAi）具有高特异性和环境安全性，是新一代农药开发的一个很有前景的平台。在后基因组时代，昆虫基因组学、传递系统和生物信息学的快速发展极大地促进了基于rna的害虫防治策略的发展。本文综述了RNAi机制阐明、靶基因选择以及宿主诱导基因沉默（HIGS）、喷雾诱导基因沉默（SIGS）和病毒诱导基因沉默（VIGS）等传递途径的研究进展。关键的挑战-如物种间变异的RNAi效率，dsRNA降解，脱靶效应-进行了详细的讨论。机器学习（ML）和全基因组筛选在优化siRNA设计和降低生态风险方面发挥着关键作用。商业产品，如ledproona和MON87411玉米，证明了基于rna的农药的实际潜力。未来的成功将取决于整合比较基因组学、基于ml的脱靶预测和生态风险评估框架，以确保应用的安全性和可持续性。

引用次数: 0

Antibiotics pollution in cropland and crops: A comprehensive review 农田和作物中的抗生素污染：综述

Advanced Agrochem

Pub Date : 2025-12-01 DOI: 10.1016/j.aac.2025.05.002

Jiahao He

Production of synthetic antibiotics has rapidly expanded to meet the increasing demands in human healthcare, animal husbandry, and agriculture. Meanwhile, however, substantial quantities of untreated antibiotics entered the agricultural environment through animal waste, reclaimed wastewater, or biosolids. The existence of drugs in farmland will not only have adverse effects on plant growth and productivity but also lead to antibiotics accumulation and drug resistance. To address this emerging drug contamination issue, this article conducts an in-depth and comprehensive analysis of the research on antibiotic pollution in cropland and crops within 20 years. In this article, the bioaccumulation mechanisms of antibiotic in crops were systematically analyzed and discussed, with emphasis on the effects of important influencing factors such as the physico-chemical properties of antibiotics, cultivation environment, and plant morphology. Additionally, this article briefly discusses the various antibiotic extraction and analytical methods, as well as calculation indexes on human risk assessment. At last, the author further analyzed the environmental challenge of antibiotic resistance and provided insights into pollution remediation pathways for future research.

合成抗生素的生产迅速扩大，以满足人类医疗保健、畜牧业和农业日益增长的需求。然而，与此同时，大量未经处理的抗生素通过动物粪便、再生废水或生物固体进入农业环境。农田中药物的存在不仅会对植物生长和生产力产生不利影响，还会导致抗生素的积累和耐药性。为了解决这一新兴的药物污染问题，本文对近20年来农田和作物中抗生素污染的研究进行了深入全面的分析。本文系统地分析和探讨了抗生素在作物中的生物积累机制，重点讨论了抗生素的理化性质、栽培环境和植物形态等重要影响因素对抗生素在作物中的生物积累的影响。此外，本文还简要讨论了各种抗生素的提取和分析方法，以及人类风险评估的计算指标。最后，作者进一步分析了抗生素耐药性的环境挑战，并为未来的研究提供了污染修复途径。

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引用次数: 0

Nature biotechnology: Scalable production of secondary metabolites from Streptomyces via a novel regulatory system 自然生物技术：链霉菌次生代谢物通过一个新的调控系统的规模化生产

Advanced Agrochem

Pub Date : 2025-12-01 DOI: 10.1016/j.aac.2025.10.001

Yanjun Zhang, Jianyang Li, Jianjun Zhang

In the world of microorganisms, the genud Streptomyces is renowned as a “natural pharmacy”. This genus of bacteria is the primary source of clinical antibiotics, with approximately two-thirds of antibiotics derived from it. However, industrial production faces challenges such as low yields and complex regulation. This study introduces the Streptomyces multiplexed artificial control system (SMARTS): a novel “plug-and-play” dynamic regulatory framework integrating trigger, stabilizer, and multiplexer modules. This enables the cross-species, predictable, and scalable production of secondary metabolites. Evolutionary analysis of 521 quorum-sensing receptors revealed conserved DNA-binding domains, informing the design of a universal trigger. SMARTS efficiently and robustly produced baiweimycin in a 120 m³ industrial fermenter, a process validated through a closed-loop pipeline ranging from molecular mechanisms to field applications. Implementing orthogonal control and hierarchical optimization enhances the efficiency of metabolic engineering and sheds light on the evolution of Streptomyces quorum sensing. This breakthrough offers a scalable solution for industrial production and advances synthetic biology, with significant implications for agriculture, pharmaceuticals, and global health.

在微生物界，链霉菌被誉为“天然药剂”。这种细菌属是临床抗生素的主要来源，大约三分之二的抗生素来源于它。然而，工业生产面临着诸如低产量和复杂监管等挑战。本研究介绍了链霉菌多路人工控制系统（SMARTS）：一种新型的“即插即用”动态调节框架，集成了触发器、稳定器和多路器模块。这使得次生代谢物的跨物种、可预测和可扩展生产成为可能。521个群体感应受体的进化分析揭示了保守的dna结合域，为通用触发器的设计提供了信息。SMARTS在120立方米的工业发酵罐中高效、稳定地生产白威霉素，该工艺通过从分子机制到现场应用的闭环管道进行了验证。通过正交控制和层次优化，提高了代谢工程的效率，为链霉菌群体感应的进化提供了新的思路。这一突破为工业生产提供了可扩展的解决方案，并推进了合成生物学，对农业、制药和全球健康具有重大影响。

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引用次数: 0