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Li Y, Li Z, Zhang F, Li S, Gu Y, Tian W, Tian W, Wang J, Wen J, Li J. Integrated evolutionary pattern analyses reveal multiple origins of steroidal saponins in plants. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:823-839. [PMID: 37522396 DOI: 10.1111/tpj.16411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 07/19/2023] [Accepted: 07/22/2023] [Indexed: 08/01/2023]
Abstract
Steroidal saponins are a class of specialized metabolites essential for plant's response to biotic and abiotic stresses. They are also important raw materials for the industrial production of steroid drugs. Steroidal saponins are present in some monocots, such as Dioscorea and Paris, but their distribution, origin, and evolution in plants remain poorly understood. By reconstructing the evolutionary history of the steroidal saponin-associated module (SSAM) in plants, we reveal that the steroidal saponin pathway has its origin in Asparagus and Dioscorea. Through evaluating the distribution and evolutionary pattern of steroidal saponins in angiosperms, we further show that steroidal saponins originated multiple times in angiosperms, and exist in early diverged lineages of certain monocot lineages including Asparagales, Dioscoreales, and Liliales. In these lineages, steroidal saponins are synthesized through the high copy and/or high expression mechanisms of key genes in SSAM. Together with shifts in gene evolutionary rates and amino acid usage, these molecular mechanisms shape the current distribution and diversity of steroidal saponins in plants. Consequently, our results provide new insights into the distribution, diversity and evolutionary history of steroidal saponins in plants, and enhance our understanding of plants' resistance to abiotic and biotic stresses. Additionally, fundamental understanding of the steroidal saponin biosynthesis will facilitate their industrial production and pharmacological applications.
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Affiliation(s)
- Yi Li
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Zihao Li
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Furui Zhang
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Song Li
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Yongbing Gu
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Weijun Tian
- Yunnan Baotian Agricultural Technology Co., Ltd, Kunming, 650101, China
| | - Weirong Tian
- Yunnan Baotian Agricultural Technology Co., Ltd, Kunming, 650101, China
| | - Jianbo Wang
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Jun Wen
- Department of Botany, National Museum of Natural History, Smithsonian Institution, Washington, 20013-7012, DC, USA
| | - Jiaru Li
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China
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Belcour A, Got J, Aite M, Delage L, Collén J, Frioux C, Leblanc C, Dittami SM, Blanquart S, Markov GV, Siegel A. Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe. Genome Res 2023; 33:972-987. [PMID: 37468308 PMCID: PMC10629481 DOI: 10.1101/gr.277056.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 05/23/2023] [Indexed: 07/21/2023]
Abstract
Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.
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Affiliation(s)
- Arnaud Belcour
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
| | - Jeanne Got
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Méziane Aite
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Ludovic Delage
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Jonas Collén
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Simon M Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Gabriel V Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
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