From data to discovery: leveraging big data in plant natural products biosynthesis research

Abstract

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.