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, 15 (2), e0228675
eCollection

Resistance Associated Metabolite Profiling of Aspergillus Leaf Spot in Cotton Through Non-Targeted Metabolomics

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Resistance Associated Metabolite Profiling of Aspergillus Leaf Spot in Cotton Through Non-Targeted Metabolomics

Maria Khizar et al. PLoS One.

Abstract

Aspergillus tubingensis is an important pathogen of economically important crops. Different biotic stresses strongly influence the balance of metabolites in plants. The aim of this study was to understand the function and response of resistance associated metabolites which, in turn are involved in many secondary metabolomics pathways to influence defense mechanism of cotton plant. Analysis of non-targeted metabolomics using ultra high performance liquid chromatography-mass spectrometry (UPLC-MS) revealed abundant accumulation of key metabolites including flavonoids, phenylpropanoids, terpenoids, fatty acids and carbohydrates, in response to leaf spot of cotton. The principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and partial least squares discriminant analysis (PLS-DA) score plots illustrated the evidences of variation between two varieties of cotton under mock and pathogen inoculated treatments. Primary metabolism was affected by the up regulation of pyruvate and malate and by the accumulation of carbohydrates like cellobiose and inulobiose. Among 241 resistance related (RR) metabolites, 18 were identified as resistance related constitutive (RRC) and 223 as resistance related induced (RRI) metabolites. Several RRI metabolites, identified in the present study were the precursors for many secondary metabolic pathways. These included phenylpropanoids (stilbenes and furanocoumarin), flavonoids (phlorizin and kaempferol), alkaloids (indolizine and acetylcorynoline) and terpenoids (azelaic acid and oleanolic acid). Our results demonstrated that secondary metabolism, primary metabolism and energy metabolism were more active in resistant cultivar, as compared to sensitive cultivar. Differential protein and fatty acid metabolism was also depicted in both cultivars. Accumulation of these defense related metabolites in resistant cotton cultivar and their suppression in susceptible cotton cultivar revealed the reason of their respective tolerance and susceptibility against A. tubingensis.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Cotton leaf spot caused by A. tubingensis.
(A) NIA- Sadori (resistant variety) (B) CIM-573 (susceptible variety) (C) NIA-Sadori (mock inoculated) (D) CIM-573 (mock inoculated).
Fig 2
Fig 2. Measurement of disease severity in V1 = CIM-573 variety and V2 = NIA-Sadori variety.
Fig 3
Fig 3. Principal Component analysis (A) and 2D Scores plot (B) in cotton leaves under control (mock) and treated (pathogen inoculated) conditions.
Mock and treated samples formed separate groups, indicating an altered state of metabolite levels in the leaves. Slight overlapping with each other was also observed.
Fig 4
Fig 4. OPLS-DA plots of four data sets: Resistant Mock vs Susceptible Mock, Resistant Treated vs Susceptible Treated, Susceptible Mock vs Susceptible Treated, Resistant Treated vs Resistant Mock.
Fig 5
Fig 5. PLS-DA of four data sets: Resistant Mock vs Susceptible Mock, Resistant Treated vs Susceptible Treated, Susceptible Mock vs Susceptible Treated, Resistant Treated vs Resistant Mock.
Fig 6
Fig 6. Volcano maps of four data sets: Resistant Mock vs Susceptible Mock, Resistant Treated vs Susceptible Treated, Susceptible Mock vs Susceptible Treated, Resistant Treated vs Resistant Mock.
Fig 7
Fig 7. Venn diagram of differential metabolites in cotton leaf, after pathogen infection.
Fig 8
Fig 8. Hierarchial clustering showing heat map of RRC metabolites with fold change > 1 in the Mock treatments (R_Mock vs. S_Mock) and their response in other treatments generated using Metaboanalyst software.
Red and green colors represent up and down regulation, respectively. Columns are exhibiting samples and rows are exhibiting metabolites here.
Fig 9
Fig 9. Hierarchial clustering showing heat map of top 60 RRI metabolites of resistant and susceptible varieties, induced by the infection of A. tubingensis, generated using Metaboanalyst software.
Red and green colors represent up and down regulation, respectively. Columns are presenting samples and rows are exhibiting metabolites here. Clustering is evident from the shown dendrograms.

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Grant support

This work was supported by: Maria Khizar, IRSIP-39-BMS-41, Higher education commission, Pakistan, https://hec.gov.pk/english/scholarshipsgrants/IRSIP/Pages/default.aspx. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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