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Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components
Plant phenomics aims to perform high-throughput, rapid, and accurate measurement of plant traits, facilitating the identification of desirable traits and optimal genotypes for crop breeding. Salvia miltiorrhiza (Danshen) roots possess remarkable therapeutic effect on cardiovascular diseases, with hu...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545446/ https://www.ncbi.nlm.nih.gov/pubmed/37791248 http://dx.doi.org/10.34133/plantphenomics.0098 |
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author | Chen, Junfeng Wang, Yun Di, Peng Wu, Yulong Qiu, Shi Lv, Zongyou Qiao, Yuqi Li, Yajing Tan, Jingfu Chen, Weixu Yu, Ma Wei, Ping Xiao, Ying Chen, Wansheng |
author_facet | Chen, Junfeng Wang, Yun Di, Peng Wu, Yulong Qiu, Shi Lv, Zongyou Qiao, Yuqi Li, Yajing Tan, Jingfu Chen, Weixu Yu, Ma Wei, Ping Xiao, Ying Chen, Wansheng |
author_sort | Chen, Junfeng |
collection | PubMed |
description | Plant phenomics aims to perform high-throughput, rapid, and accurate measurement of plant traits, facilitating the identification of desirable traits and optimal genotypes for crop breeding. Salvia miltiorrhiza (Danshen) roots possess remarkable therapeutic effect on cardiovascular diseases, with huge market demands. Although great advances have been made in metabolic studies of the bioactive metabolites, investigation for S. miltiorrhiza roots on other physiological aspects is poor. Here, we developed a framework that utilizes image feature extraction software for in-depth phenotyping of S. miltiorrhiza roots. By employing multiple software programs, S. miltiorrhiza roots were described from 3 aspects: agronomic traits, anatomy traits, and root system architecture. Through K-means clustering based on the diameter ranges of each root branch, all roots were categorized into 3 groups, with primary root-associated key traits. As a proof of concept, we examined the phenotypic components in a series of randomly collected S. miltiorrhiza roots, demonstrating that the total surface of root was the best parameter for the biomass prediction with high linear regression correlation (R(2) = 0.8312), which was sufficient for subsequently estimating the production of bioactive metabolites without content determination. This study provides an important approach for further grading of medicinal materials and breeding practices. |
format | Online Article Text |
id | pubmed-10545446 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-105454462023-10-03 Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components Chen, Junfeng Wang, Yun Di, Peng Wu, Yulong Qiu, Shi Lv, Zongyou Qiao, Yuqi Li, Yajing Tan, Jingfu Chen, Weixu Yu, Ma Wei, Ping Xiao, Ying Chen, Wansheng Plant Phenomics Research Article Plant phenomics aims to perform high-throughput, rapid, and accurate measurement of plant traits, facilitating the identification of desirable traits and optimal genotypes for crop breeding. Salvia miltiorrhiza (Danshen) roots possess remarkable therapeutic effect on cardiovascular diseases, with huge market demands. Although great advances have been made in metabolic studies of the bioactive metabolites, investigation for S. miltiorrhiza roots on other physiological aspects is poor. Here, we developed a framework that utilizes image feature extraction software for in-depth phenotyping of S. miltiorrhiza roots. By employing multiple software programs, S. miltiorrhiza roots were described from 3 aspects: agronomic traits, anatomy traits, and root system architecture. Through K-means clustering based on the diameter ranges of each root branch, all roots were categorized into 3 groups, with primary root-associated key traits. As a proof of concept, we examined the phenotypic components in a series of randomly collected S. miltiorrhiza roots, demonstrating that the total surface of root was the best parameter for the biomass prediction with high linear regression correlation (R(2) = 0.8312), which was sufficient for subsequently estimating the production of bioactive metabolites without content determination. This study provides an important approach for further grading of medicinal materials and breeding practices. AAAS 2023-10-02 /pmc/articles/PMC10545446/ /pubmed/37791248 http://dx.doi.org/10.34133/plantphenomics.0098 Text en Copyright © 2023 Junfeng Chen et al. https://creativecommons.org/licenses/by/4.0/Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Chen, Junfeng Wang, Yun Di, Peng Wu, Yulong Qiu, Shi Lv, Zongyou Qiao, Yuqi Li, Yajing Tan, Jingfu Chen, Weixu Yu, Ma Wei, Ping Xiao, Ying Chen, Wansheng Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components |
title | Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components |
title_full | Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components |
title_fullStr | Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components |
title_full_unstemmed | Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components |
title_short | Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components |
title_sort | phenotyping of salvia miltiorrhiza roots reveals associations between root traits and bioactive components |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545446/ https://www.ncbi.nlm.nih.gov/pubmed/37791248 http://dx.doi.org/10.34133/plantphenomics.0098 |
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