Untargeted Metabolomics of Alternaria solani-Challenged Wild Tomato Species Solanum cheesmaniae Revealed Key Metabolite Biomarkers and Insight into Altered Metabolic Pathways

Abstract

Untargeted metabolomics of moderately resistant wild tomato species Solanum cheesmaniae revealed an altered metabolite profile in plant leaves in response to Alternaria solani pathogen. Leaf metabolites were significantly differentiated in non-stressed versus stressed plants. The samples were discriminated not only by the presence/absence of specific metabolites as distinguished markers of infection, but also on the basis of their relative abundance as important concluding factors. Annotation of metabolite features using the Arabidopsis thaliana (KEGG) database revealed 3371 compounds with KEGG identifiers belonging to biosynthetic pathways including secondary metabolites, cofactors, steroids, brassinosteroids, terpernoids, and fatty acids. Annotation using the Solanum lycopersicum database in PLANTCYC PMN revealed significantly upregulated (541) and downregulated (485) features distributed in metabolite classes that appeared to play a crucial role in defense, infection prevention, signaling, plant growth, and plant homeostasis to survive under stress conditions. The orthogonal partial least squares discriminant analysis (OPLS-DA), comprising a significant fold change (≥2.0) with VIP score (≥1.0), showed 34 upregulated biomarker metabolites including 5-phosphoribosylamine, kaur-16-en-18-oic acid, pantothenate, and O-acetyl-L-homoserine, along with 41 downregulated biomarkers. Downregulated metabolite biomarkers were mapped with pathways specifically known for plant defense, suggesting their prominent role in pathogen resistance. These results hold promise for identifying key biomarker metabolites that contribute to disease resistive metabolic traits/biosynthetic routes. This approach can assist in mQTL development for the stress breeding program in tomato against pathogen interactions.

Metabolomics of early blight (Alternaria solani) susceptible tomato (Solanum lycopersicum) unfolds key biomarker metabolites and involved metabolic pathways

“…For MS/MS, scan range was 100–2000 m/z , isolation offset m/z 0.5, collision energy (CE) 25-45 eV, maximum IT 50 ms, loop count 5, MSX count 1, resolution 35,000, microscans 1 and isolation window m/z 2.0. Further, Data Dependent Acquisition (DDA) and automated precursor ion selection methods were described by Singh et al (2023) 75 . For MS 2 spectra, two blanks and three quality checks (QCs) were ensured during system conditioning.…”

“…The raw data generated from LC–MS/MS was subjected to Compound Discoverer 3.3 (Thermo Scientific, USA) for data analysis as per the parameters described previously 75 . The raw files were pre-processed and analyzed to yield peak intensity, retention time ( rt ), abundance, missing values and adjoint ion combinations after appropriate normalization of metabolite features ( m/z ).…”

Comparative analysis of defensive secondary metabolites in wild teosinte and cultivated maize under flooding and herbivory stress

“…In addition, metabolites with hormonal functions, including jasmonic acid and ethylene, may also accumulate during flooding to coordinate root developmental responses (Raya‐González et al, 2012; Cai et al, 2014; Borrego & Kolomiets, 2016; Mekawy et al, 2018; Yildiztugay et al, 2020; Xu et al, 2020). To evaluate changes in the metabolic profile of plant tissues experiencing environmental stress, untargeted LC‐MS‐based metabolomics remains a powerful tool (Khan et al, 2019; Christensen et al, 2021; Kumar et al, 2021; Singh et al, 2023).…”