Metabolic Profiling to Identify the Latent Infection of Strawberry by
In plant-pathogen interaction systems, plant metabolism is usually agitated in the early stages of infection and much before visible symptoms appear. To identify the latent infection of strawberry by Botrytis cinerea by metabolome profiling, a metabolomics method based on gas chromatography and mass...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2019-04-01
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| Series: | Evolutionary Bioinformatics |
| Online Access: | https://doi.org/10.1177/1176934319838518 |
| _version_ | 1818859107002613760 |
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| author | Zhihong Hu Xunian Chang Tan Dai Lei Li Panqing Liu Guozhen Wang Pengfei Liu Zhongqiao Huang Xili Liu |
| author_facet | Zhihong Hu Xunian Chang Tan Dai Lei Li Panqing Liu Guozhen Wang Pengfei Liu Zhongqiao Huang Xili Liu |
| author_sort | Zhihong Hu |
| collection | DOAJ |
| description | In plant-pathogen interaction systems, plant metabolism is usually agitated in the early stages of infection and much before visible symptoms appear. To identify the latent infection of strawberry by Botrytis cinerea by metabolome profiling, a metabolomics method based on gas chromatography and mass spectrometry was applied to identify the affected metabolites and discriminate diseased plants from healthy ones. An orthogonal partial least squares (OPLS) score plot showed that the metabolic profiling well separated B. cinerea -infected strawberry plants at 2, 5, and 7 days after infection from non-infected healthy plants. Combined analysis of variance (ANOVA) and OPLS analysis revealed candidate biomarkers of plant resistance and of infection and expansion of the pathogen in the plants. Among them, hexadecanoic acid, octadecanoic acid, sucrose, β-lyxopyranose, melibiose, and 1,1,4a-Trimethyl-5,6-dimethylenedecahydronaphthalene were closely related to the early stage of disease development when symptoms were not visible. A discrimination method that could distinguish Botrytis gray mold diseased strawberry plants from healthy ones was established based on the partial least squares discriminant analysis (PLS-DA) model with a correct recognition accuracy of 100%. This research offers a good application of metabolome profiling for early diagnosis of plant disease and interaction mechanism exploration. |
| first_indexed | 2024-12-19T09:06:55Z |
| format | Article |
| id | doaj.art-4d6d7e90c1194961b1d80ef94ab99f07 |
| institution | Directory Open Access Journal |
| issn | 1176-9343 |
| language | English |
| last_indexed | 2024-12-19T09:06:55Z |
| publishDate | 2019-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Evolutionary Bioinformatics |
| spelling | doaj.art-4d6d7e90c1194961b1d80ef94ab99f072022-12-21T20:28:19ZengSAGE PublishingEvolutionary Bioinformatics1176-93432019-04-011510.1177/1176934319838518Metabolic Profiling to Identify the Latent Infection of Strawberry byZhihong HuXunian ChangTan DaiLei LiPanqing LiuGuozhen WangPengfei LiuZhongqiao HuangXili LiuIn plant-pathogen interaction systems, plant metabolism is usually agitated in the early stages of infection and much before visible symptoms appear. To identify the latent infection of strawberry by Botrytis cinerea by metabolome profiling, a metabolomics method based on gas chromatography and mass spectrometry was applied to identify the affected metabolites and discriminate diseased plants from healthy ones. An orthogonal partial least squares (OPLS) score plot showed that the metabolic profiling well separated B. cinerea -infected strawberry plants at 2, 5, and 7 days after infection from non-infected healthy plants. Combined analysis of variance (ANOVA) and OPLS analysis revealed candidate biomarkers of plant resistance and of infection and expansion of the pathogen in the plants. Among them, hexadecanoic acid, octadecanoic acid, sucrose, β-lyxopyranose, melibiose, and 1,1,4a-Trimethyl-5,6-dimethylenedecahydronaphthalene were closely related to the early stage of disease development when symptoms were not visible. A discrimination method that could distinguish Botrytis gray mold diseased strawberry plants from healthy ones was established based on the partial least squares discriminant analysis (PLS-DA) model with a correct recognition accuracy of 100%. This research offers a good application of metabolome profiling for early diagnosis of plant disease and interaction mechanism exploration.https://doi.org/10.1177/1176934319838518 |
| spellingShingle | Zhihong Hu Xunian Chang Tan Dai Lei Li Panqing Liu Guozhen Wang Pengfei Liu Zhongqiao Huang Xili Liu Metabolic Profiling to Identify the Latent Infection of Strawberry by Evolutionary Bioinformatics |
| title | Metabolic Profiling to Identify the Latent Infection of Strawberry by |
| title_full | Metabolic Profiling to Identify the Latent Infection of Strawberry by |
| title_fullStr | Metabolic Profiling to Identify the Latent Infection of Strawberry by |
| title_full_unstemmed | Metabolic Profiling to Identify the Latent Infection of Strawberry by |
| title_short | Metabolic Profiling to Identify the Latent Infection of Strawberry by |
| title_sort | metabolic profiling to identify the latent infection of strawberry by |
| url | https://doi.org/10.1177/1176934319838518 |
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