Mots-Clés
bioinformatics
mebabolomics
plant-multi stress
mulit-omics integration
machine learning
knowledge graph
Description
Exploring metabolomics data to model tomato response to multiple stresses
Context and objectives:
Multiple infections affecting a single plant or crop are recognized to be common in plant disease epidemic (1). However, usually diseases in plants are studied as the results of the interaction between one host and a single pathogen. Furthermore, during pathogens attacks, those interactions are based on a molecular dialogue between the pathogen and its host that occur at different biological layers, altogether concurring for a successful or unsuccessful infection (2). Despite the complexity of plant immune response involves several layers of molecular regulations, usually only one level of regulation is considered. The biotechnological and digital advances of the last decade offer a great opportunity to overcome this stalemate. The flourishing of omics techniques has led to the possibility of studying complex biological systems, through the analysis of its content at the molecular level. Transcriptomics is by far the most used omics to study plant response to stress providing details about gene transcription, regulation, and quantitative change in expression in a cost-effective and straightforward analysis fashion. Lately, more and more evidences show that biotic factors stimulate multi-gene responses by making modifications in the accumulation of the primary and secondary metabolites (3). Although, primary metabolites and plant-derived secondary metabolites also play a fundamental role in plant defense response, metabolomics investigations have just recently started (3). The metabolome is not only the last downstream product of the genome but also the fingerprint of many internal and external stressors, and therefore it can help to explain the interaction between the biological systems and their environment and all related perturbations. Therefore, metabolomics analyses are becoming pivotal, in complement to the other omics, to better understand the molecular basis of plant stress response.
As biological model we will focus on tomato (S. lycopersicum) since it is one of the most economically important vegetables throughout the world and because during cultivation or in post-harvest storage, it is susceptible to more than 200 diseases caused by several different pathogens. Our team is leading the development of POMOdOROO, a curated database collecting transcriptomic datasets from Solanum lycopersicum subjected to a wide range of biotic and abiotic stresses. Leveraging this resource, the objectives of the internship will be:
1) Data collection : literature mining to collect metabolomics datasets of tomato sujected to environmental stresses
2) Data curation: development of a standardized pipeline for quality control and processing of plants metabolomics data
3) Data integration: development of a novel machine learning method, based on TomTom (4) and HIVE (5) developed in the team, to integrate transcriptomics and metabolomics data
The outcome of the project will be the identification of genes which show coordinated expression shifts in tomato linked to metabolic reprogramming across different pathogens infections.
Tutors:
Silvia Bottini, Junior Professor Chair INRAe/UniCA – Team leader; team SMILE at the Institut Sophia Agrobiotech, silvia.bottini@inrae.fr
Maxime Multari, PhD student; team SMILE at the Institut Sophia Agrobiotech, silvia.bottini@inrae.fr
Where :
Institut Sophia Agrobiotech, Sophia-Antipolis: https://institut-sophia-agrobiotech.paca.hub.inrae.fr/equipes-isa/smile
References
1. Multi-infections, competitive interactions, and pathogen coexistence. [cited 2024 Sep 20]; Available from: https://bsppjournals.onlinelibrary.wiley.com/doi/10.1111/ppa.13469
2. Wang Y, Pruitt RN, Nürnberger T, Wang Y. Evasion of plant immunity by microbial pathogens. Nat Rev Microbiol. 2022 Aug;20(8):449–64.
3. Salam U, Ullah S, Tang ZH, Elateeq AA, Khan Y, Khan J, et al. Plant Metabolomics: An Overview of the Role of Primary and Secondary Metabolites against Different Environmental Stress Factors. Life. 2023 Mar 6;13(3):706.
4. Multari M, Carriere M, Amoros-Gabarron X, Damy A, Lobentanzer S, Saez-Rodriguez J, Jaubert S, Dugourd A, Bottini S. A knowledge graph and topological data analysis framework to disentangle the tomato-multi pathogens complex gene regulatory network. Available from: https://www.biorxiv.org/content/10.1101/2025.04.09.647963v1
5. Calia G, Marguerit S, Mota APZ, Vidal M, Schuler H, Brasileiro ACM, Guimares P, Bottini S. Modelling single-stress omics integration with HIVE enables the identification of responding signatures to multifactorial stress combinations in plants. Available from: https://www.biorxiv.org/content/10.1101/2024.03.04.583290v3