McConnachie Matthew, Nguyen Tuan-Anh Minh, Kim Truc, Nguyen Trinh-Don, Dang Thu-Thuy T
Department of Chemistry, Irving K. Barber Faculty of Science, University of British Columbia, Kelowna, British Columbia, V1V 1V7, Canada.
Plant J. 2025 Jun;122(6):e70288. doi: 10.1111/tpj.70288.
Plant natural products or specialized metabolites play a vital role in drug discovery and development, with many clinically important derivatives such as the anticancer drugs topotecan (derived from the natural alkaloid camptothecin) and etoposide (derived from the natural polyphenol podophyllotoxin). Remarkable advances in understanding plant natural product metabolism have been achieved at an unprecedented pace over the past 15 years. The integration of high-throughput technologies in genomics, transcriptomics, and metabolomics has generated vast datasets that provide a more comprehensive understanding of plant metabolism. Additionally, advances in computational tools, machine learning, and data analytics have played a crucial role in processing and interpreting the massive amounts of newly available data, enabling researchers to uncover intricate regulatory networks and identify key components of biosynthetic pathways. This review navigates the evolving landscape of plant biosynthetic pathway elucidation accelerated by innovative multidisciplinary strategies that capitalize on big data. We highlight recent advances in plant-specialized biosynthesis that illustrate how big data are increasingly leveraged to unravel the complexities of plant metabolism.
植物天然产物或特殊代谢产物在药物发现和开发中发挥着至关重要的作用，有许多具有临床重要性的衍生物，如抗癌药物拓扑替康（源自天然生物碱喜树碱）和依托泊苷（源自天然多酚鬼臼毒素）。在过去15年里，对植物天然产物代谢的理解取得了前所未有的显著进展。基因组学、转录组学和代谢组学中高通量技术的整合产生了大量数据集，从而对植物代谢有了更全面的理解。此外，计算工具、机器学习和数据分析方面的进展在处理和解释大量新获得的数据方面发挥了关键作用，使研究人员能够揭示复杂的调控网络并识别生物合成途径的关键成分。本综述探讨了由利用大数据的创新多学科策略加速的植物生物合成途径阐明的不断演变的格局。我们重点介绍了植物特殊生物合成方面的最新进展，这些进展说明了大数据如何越来越多地被用于揭示植物代谢的复杂性。
