Ankan Das, Sandip Debnath, Sourish Pramanik, Fakhrul Islam Monshi, Mehdi Rahimi
{"title":"生物数字反馈回路系统:预测基因组学、基因组编辑和人工智能驱动的现象合成的协同整合,用于下一代食用和药用蘑菇育种","authors":"Ankan Das, Sandip Debnath, Sourish Pramanik, Fakhrul Islam Monshi, Mehdi Rahimi","doi":"10.1007/s10482-025-02176-8","DOIUrl":null,"url":null,"abstract":"<div><p>Edible mushrooms face persistent challenges in yield optimization, bioactive compound production, and climate resilience that conventional breeding methods struggle to address. Traditional approaches such as cross-breeding, protoplast fusion, and mutagenesis are limited by genetic noise, laborious screening, and unstable trait inheritance. This review proposes a transformative paradigm built upon converging advances in molecular biology and data science: the bio-digital feedback loop (BDFL) framework, integrating multi-omics, CRISPR-engineered chassis strains, and predictive phenomics for precision mushroom breeding. Our framework employs multi-omics to decipher gene networks governing critical traits, such as substrate degradation enzymes, developmental synchrony regulators, and secondary metabolite pathways. CRISPR-Cas9 and synthetic biology tools then deploy these insights to verify and design modular gene circuits in pre-engineered \"plug-and-play\" chassis strains, enabling conflict-free stacking of desirable traits. Artificial intelligence serves as the linchpin, not only automating high-throughput phenotyping through advanced imaging but also accelerating the entire breeding cycle by predicting trait heritability from omics data and optimizing the design of CRISPR guide RNAs and genetic constructs for efficient editing. The BDFL we describe iteratively refines strains by feeding phenomics data back into AI algorithms, enabling rapid trait optimization cycles. This transcends the trial-and-error limitations of classical methods, accelerating development of climate-smart mushrooms for circular bioeconomies including strains engineered to thrive on agricultural waste, overproduce immunomodulatory compounds, or resist emerging pathogens. The integration of predictive genomics, AI-driven phenomics, and CRISPR-edited chassis strains heralds a new era of precision mycology, where mushrooms are computationally designed as sustainable solutions for global food security, pharmaceutical innovation, and ecological resilience, ultimately transforming fungi into programmable biological factories tailored to address pressing agricultural and ecological challenges.</p></div>","PeriodicalId":50746,"journal":{"name":"Antonie Van Leeuwenhoek International Journal of General and Molecular Microbiology","volume":"118 11","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bio-digital feedback loop systems: a synergistic integration of predictive genomics, genome editing, and AI-driven phenomic synthesis for next-generation edible and medicinal mushroom breeding\",\"authors\":\"Ankan Das, Sandip Debnath, Sourish Pramanik, Fakhrul Islam Monshi, Mehdi Rahimi\",\"doi\":\"10.1007/s10482-025-02176-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Edible mushrooms face persistent challenges in yield optimization, bioactive compound production, and climate resilience that conventional breeding methods struggle to address. Traditional approaches such as cross-breeding, protoplast fusion, and mutagenesis are limited by genetic noise, laborious screening, and unstable trait inheritance. This review proposes a transformative paradigm built upon converging advances in molecular biology and data science: the bio-digital feedback loop (BDFL) framework, integrating multi-omics, CRISPR-engineered chassis strains, and predictive phenomics for precision mushroom breeding. Our framework employs multi-omics to decipher gene networks governing critical traits, such as substrate degradation enzymes, developmental synchrony regulators, and secondary metabolite pathways. CRISPR-Cas9 and synthetic biology tools then deploy these insights to verify and design modular gene circuits in pre-engineered \\\"plug-and-play\\\" chassis strains, enabling conflict-free stacking of desirable traits. Artificial intelligence serves as the linchpin, not only automating high-throughput phenotyping through advanced imaging but also accelerating the entire breeding cycle by predicting trait heritability from omics data and optimizing the design of CRISPR guide RNAs and genetic constructs for efficient editing. The BDFL we describe iteratively refines strains by feeding phenomics data back into AI algorithms, enabling rapid trait optimization cycles. This transcends the trial-and-error limitations of classical methods, accelerating development of climate-smart mushrooms for circular bioeconomies including strains engineered to thrive on agricultural waste, overproduce immunomodulatory compounds, or resist emerging pathogens. The integration of predictive genomics, AI-driven phenomics, and CRISPR-edited chassis strains heralds a new era of precision mycology, where mushrooms are computationally designed as sustainable solutions for global food security, pharmaceutical innovation, and ecological resilience, ultimately transforming fungi into programmable biological factories tailored to address pressing agricultural and ecological challenges.</p></div>\",\"PeriodicalId\":50746,\"journal\":{\"name\":\"Antonie Van Leeuwenhoek International Journal of General and Molecular Microbiology\",\"volume\":\"118 11\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2025-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Antonie Van Leeuwenhoek International Journal of General and Molecular Microbiology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10482-025-02176-8\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Antonie Van Leeuwenhoek International Journal of General and Molecular Microbiology","FirstCategoryId":"99","ListUrlMain":"https://link.springer.com/article/10.1007/s10482-025-02176-8","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
Bio-digital feedback loop systems: a synergistic integration of predictive genomics, genome editing, and AI-driven phenomic synthesis for next-generation edible and medicinal mushroom breeding
Edible mushrooms face persistent challenges in yield optimization, bioactive compound production, and climate resilience that conventional breeding methods struggle to address. Traditional approaches such as cross-breeding, protoplast fusion, and mutagenesis are limited by genetic noise, laborious screening, and unstable trait inheritance. This review proposes a transformative paradigm built upon converging advances in molecular biology and data science: the bio-digital feedback loop (BDFL) framework, integrating multi-omics, CRISPR-engineered chassis strains, and predictive phenomics for precision mushroom breeding. Our framework employs multi-omics to decipher gene networks governing critical traits, such as substrate degradation enzymes, developmental synchrony regulators, and secondary metabolite pathways. CRISPR-Cas9 and synthetic biology tools then deploy these insights to verify and design modular gene circuits in pre-engineered "plug-and-play" chassis strains, enabling conflict-free stacking of desirable traits. Artificial intelligence serves as the linchpin, not only automating high-throughput phenotyping through advanced imaging but also accelerating the entire breeding cycle by predicting trait heritability from omics data and optimizing the design of CRISPR guide RNAs and genetic constructs for efficient editing. The BDFL we describe iteratively refines strains by feeding phenomics data back into AI algorithms, enabling rapid trait optimization cycles. This transcends the trial-and-error limitations of classical methods, accelerating development of climate-smart mushrooms for circular bioeconomies including strains engineered to thrive on agricultural waste, overproduce immunomodulatory compounds, or resist emerging pathogens. The integration of predictive genomics, AI-driven phenomics, and CRISPR-edited chassis strains heralds a new era of precision mycology, where mushrooms are computationally designed as sustainable solutions for global food security, pharmaceutical innovation, and ecological resilience, ultimately transforming fungi into programmable biological factories tailored to address pressing agricultural and ecological challenges.
期刊介绍:
Antonie van Leeuwenhoek publishes papers on fundamental and applied aspects of microbiology. Topics of particular interest include: taxonomy, structure & development; biochemistry & molecular biology; physiology & metabolic studies; genetics; ecological studies; especially molecular ecology; marine microbiology; medical microbiology; molecular biological aspects of microbial pathogenesis and bioinformatics.