Mingzhou Bai, Sanjie Jiang, Shanshan Chu, Yangyang Yu, Dai Shan, Chun Liu, Liang Zong, Qun Liu, Nana Liu, Weisong Xu, Zhanlong Mei, Jianbo Jian, Chi Zhang, Shancen Zhao, Tsan-Yu Chiu, Henrik Toft Simonsen
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引用次数: 0
Abstract
Background: Traditional Chinese medicine has used Peucedanum praeruptorum Dunn (Apiaceae) for a long time. Various coumarins, including the significant constituents praeruptorin (A-E), are the active constituents in the dried roots of P. praeruptorum. Previous transcriptomic and metabolomic studies have attempted to elucidate the distribution and biosynthetic network of these medicinal-valuable compounds. However, the lack of a high-quality reference genome impedes an in-depth understanding of genetic traits and thus the development of better breeding strategies.
Results: A telomere-to-telomere (T2T) genome was assembled for P. praeruptorum by combining PacBio HiFi, ONT ultra-long, and Hi-C data. The final genome assembly was approximately 1.798 Gb, assigned to 11 chromosomes with genome completeness >98%. Comparative genomic analysis suggested that P. praeruptorum experienced 2 whole-genome duplication events. By the transcriptomic and metabolomic analysis of the coumarin metabolic pathway, we presented coumarins' spatial and temporal distribution and the expression patterns of critical genes for its biosynthesis. Notably, the COSY and cytochrome P450 genes showed tandem duplications on several chromosomes, which may be responsible for the high accumulation of coumarins.
Conclusions: A T2T genome for P. praeruptorum was obtained, providing molecular insights into the chromosomal distribution of the coumarin biosynthetic genes. This high-quality genome is an essential resource for designing engineering strategies for improving the production of these valuable compounds.
期刊介绍:
GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.