Feifei Cai, Shijie Zhang, Yang Dai, Ziqi Zhao, Xinnan Fu, Qianjin Kang, Yongyong Shi, Zhuo Wang, Linquan Bai
{"title":"非模式细菌的综合多维建模确定了阿卡波糖生物合成优化的工程靶点。","authors":"Feifei Cai, Shijie Zhang, Yang Dai, Ziqi Zhao, Xinnan Fu, Qianjin Kang, Yongyong Shi, Zhuo Wang, Linquan Bai","doi":"10.1016/j.crmeth.2026.101426","DOIUrl":null,"url":null,"abstract":"<p><p>Metabolic engineering for high-value compounds such as acarbose, a diabetes drug, requires systematic understanding of metabolic regulation. Here, we applied a multi-dimensional systems biology approach in Actinoplanes sp. SE50/110, a non-model acarbose-producing bacterium. We reconstructed an improved genome-scale metabolic model (iASE1267) with expanded metabolic coverage and a MEMOTE score of 80%, enabling more accurate phenotype predictions. Using a dual-objective OptRAM strain design strategy, we identified two sets of static engineering targets, including AcbR overexpression with adenylosuccinate lyase repression, and overexpression of dTDP-glucose 4,6-dehydratase with repression of 4-(cytidine 5'-diphospho)-2-methyl-D-erythritol kinase. Time-course metabolic modeling further revealed dynamic metabolic valves-ASPO1, PC, and PYK-governing flux redistribution. Integrating these targets, we reconstructed a core transcription-metabolism network and identified two pleiotropic negative transcription factors (TFs). Experimental validation of these TFs and metabolic genes increased acarbose titers by 18%-23%. This work establishes a framework integrating static/dynamic metabolic modeling with transcriptional networks for engineering non-model microbes.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101426"},"PeriodicalIF":4.5000,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrated multi-dimensional modeling of non-model bacteria identifies engineering targets for acarbose biosynthesis optimization.\",\"authors\":\"Feifei Cai, Shijie Zhang, Yang Dai, Ziqi Zhao, Xinnan Fu, Qianjin Kang, Yongyong Shi, Zhuo Wang, Linquan Bai\",\"doi\":\"10.1016/j.crmeth.2026.101426\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Metabolic engineering for high-value compounds such as acarbose, a diabetes drug, requires systematic understanding of metabolic regulation. Here, we applied a multi-dimensional systems biology approach in Actinoplanes sp. SE50/110, a non-model acarbose-producing bacterium. We reconstructed an improved genome-scale metabolic model (iASE1267) with expanded metabolic coverage and a MEMOTE score of 80%, enabling more accurate phenotype predictions. Using a dual-objective OptRAM strain design strategy, we identified two sets of static engineering targets, including AcbR overexpression with adenylosuccinate lyase repression, and overexpression of dTDP-glucose 4,6-dehydratase with repression of 4-(cytidine 5'-diphospho)-2-methyl-D-erythritol kinase. Time-course metabolic modeling further revealed dynamic metabolic valves-ASPO1, PC, and PYK-governing flux redistribution. Integrating these targets, we reconstructed a core transcription-metabolism network and identified two pleiotropic negative transcription factors (TFs). Experimental validation of these TFs and metabolic genes increased acarbose titers by 18%-23%. This work establishes a framework integrating static/dynamic metabolic modeling with transcriptional networks for engineering non-model microbes.</p>\",\"PeriodicalId\":29773,\"journal\":{\"name\":\"Cell Reports Methods\",\"volume\":\" \",\"pages\":\"101426\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2026-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell Reports Methods\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.crmeth.2026.101426\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Reports Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.crmeth.2026.101426","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Integrated multi-dimensional modeling of non-model bacteria identifies engineering targets for acarbose biosynthesis optimization.
Metabolic engineering for high-value compounds such as acarbose, a diabetes drug, requires systematic understanding of metabolic regulation. Here, we applied a multi-dimensional systems biology approach in Actinoplanes sp. SE50/110, a non-model acarbose-producing bacterium. We reconstructed an improved genome-scale metabolic model (iASE1267) with expanded metabolic coverage and a MEMOTE score of 80%, enabling more accurate phenotype predictions. Using a dual-objective OptRAM strain design strategy, we identified two sets of static engineering targets, including AcbR overexpression with adenylosuccinate lyase repression, and overexpression of dTDP-glucose 4,6-dehydratase with repression of 4-(cytidine 5'-diphospho)-2-methyl-D-erythritol kinase. Time-course metabolic modeling further revealed dynamic metabolic valves-ASPO1, PC, and PYK-governing flux redistribution. Integrating these targets, we reconstructed a core transcription-metabolism network and identified two pleiotropic negative transcription factors (TFs). Experimental validation of these TFs and metabolic genes increased acarbose titers by 18%-23%. This work establishes a framework integrating static/dynamic metabolic modeling with transcriptional networks for engineering non-model microbes.