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

IF 1.8 3区 生物学 Q4 MICROBIOLOGY
Ankan Das, Sandip Debnath, Sourish Pramanik, Fakhrul Islam Monshi, Mehdi Rahimi
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引用次数: 0

摘要

食用菌在产量优化、生物活性化合物生产和气候适应性方面面临着传统育种方法难以解决的持续挑战。传统的杂交育种、原生质体融合和诱变等方法受到遗传噪声、费力的筛选和不稳定的性状遗传的限制。这篇综述提出了一种基于分子生物学和数据科学进步的变革范式:生物数字反馈回路(BDFL)框架,整合多组学、crispr工程底盘菌株和预测表型组学,用于精确的蘑菇育种。我们的框架采用多组学来破译控制关键性状的基因网络,如底物降解酶、发育同步调节因子和次级代谢物途径。然后,CRISPR-Cas9和合成生物学工具利用这些见解,在预先设计的“即插即用”底盘菌株中验证和设计模块化基因电路,从而实现理想性状的无冲突堆叠。人工智能是关键,不仅通过先进的成像技术自动化高通量表型,而且通过组学数据预测性状遗传力,优化CRISPR引导rna和遗传构建体的设计以进行高效编辑,从而加快整个育种周期。我们描述的BDFL通过将表型组学数据反馈给人工智能算法来迭代地改进菌株,从而实现快速的性状优化周期。这超越了传统方法的试错限制,加速了用于循环生物经济的气候智能型蘑菇的开发,包括经过改造的菌株,可以在农业废弃物中茁壮成长,过量产生免疫调节化合物,或抵抗新出现的病原体。预测基因组学、人工智能驱动的表型组学和crispr编辑的基础菌株的整合预示着一个精确真菌学的新时代,蘑菇被计算设计为全球粮食安全、制药创新和生态弹性的可持续解决方案,最终将真菌转变为可编程的生物工厂,以应对紧迫的农业和生态挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
CiteScore
5.60
自引率
11.50%
发文量
104
审稿时长
3 months
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.
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