[Defended thesis] Gaëlle Lefort: Integrating metabolomic data by quantification of high throughput data. Application to perinatal mortality in pigs

Integrating metabolomic data by quantification of high throughput data. Application to perinatal mortality in pigs
 

Hello, my name is Gaëlle Lefort. Before starting my thesis, I studied statistics and computer science. Firstly, with a DUT in statistics and business intelligence, and then in an engineering school specializing in statistics (Ecole nationale de statistique et d'analyse de l'information, Ensai).
In my final year of study, I chose to move into the field of biology, doing my final internships first at Inserm and then at Isped. I then had the opportunity to discover the world of research during a 3-year fixed-term contract at INRA. This experience made me want to go deeper into what I was doing by doing a thesis.

• Starting date: September 2019
• University: Université Toulouse 1 Capitole
• PhD school: MITT (Mathématiques, Informatique, Télécommunications de Toulouse)
• Scientific field: Maths and its application
• Thesis management: Nathalie Vialaneix, Inrae, UR MIAT
• Thesis supervisors:  Rémi Servien, Inrae, InTheRes Toulouse, Laurence Liaubet, Inrae, GenPhySE Toulouse, Hélène Quesnel, Inrae, UR Pegase Rennes
• Funding: #DigitAg – Inrae
•  #DigitAg : Cofunded PhD – Axe 5 – Challenges 2 et 4

Keywords: Metabolomics, Variable selection, Multiple testing, Data integration

Abstract: Metabolomics data are an affordable way to access fine phenotyping information. They can be acquired from simple blood (plasma), amniotic fluid or urine samples using NMR (Nuclear Magnetic Resonance) high throughput technic. However, due to a lack of method to properly link acquired spectra to metabolite quantification, they are largely underexploited. This PhD proposes to develop a new fast and efficient approach to use metabolomics NMR spectra for metabolite quantification. The results will be validated with direct quantification of metabolites from the ANR project PORCINET ANR-09-GENM-005. This project addresses the issue of perinatal mortality in pigs and the numerous datasets acquired during the project provide a rich framework to prove the efficiency of our approach for precision farming. In particular, metabolite quantification will be integrated with various phenotypes (morphology, allometry, and physiology), proteomic and transcriptomic data obtained in a complex experimental design (4 genotypes x 2 late gestational stages) in order to better understand the biological processes involved in perinatal survival of pigs. The output of this PhD will be twofold: first, we intent to develop state-of-the-art method for metabolite quantification from NMR spectra. The method will be released for public use in the form of an R package and a Galaxy module. Second, on a biological point of view, we will provide biomarkers explaining biological processes involved in piglet survival. These biomarkers will permit the selection of pig phenotypes to decrease the early death of piglets that will improve pig production competitiveness and sustainability. Because of the easy and cheap acquisition of metabolic data, our approach has great potentials for selection and will be generic enough to adapt to other types of problems.

Jury compound:

Contact : gaelle.lefort@inrae.fr​

Communications & Papers

Download the thesis manuscript

Articles

Tardivel, P., Canlet, C., Lefort, G., Tremblay-Franco, M., Debrauwer, L., Concordet, D., Servien, R. (2017). ASICS : an automatic method for identification and quantification of metabolites in NMR 1D 1H spectra. Metabolomics. https://doi.org/10.1007/s11306-017-1244-5

Lefort, G., Servien, R., Quesnel, H. et al. (2020), The maturity in fetal pigs using a multi-fluid metabolomic approach, https://doi.org/10.1038/s41598-020-76709-8

Lefort G., Liaubet L., Marty-Gasset N., Canlet C., Vialaneix N., Servien R. (2021) Joint Automatic Metabolite Identification and Quantification of a Set of 1H NMR Spectra

Conferences

Lefort, G., Liaubet, L., Canlet, C., Villa-Vialaneix, N., Servien, R. (2018). ASICS identifier et quantifier des métabolites à partir d’un spectre RMN 1H, 51èmes Journées de Statistique de la SFdS – https://hal.inrae.fr/hal-02737497/document

Lefort, G., Liaubet, L., Canlet, C., Vialaneix, N., Servien, R. (2018). ASICS identification and quantification of metabolites in complex 1H NMR spectra. European Conference on Computational Biology (ECCB 2018). Poster.

Gaëlle Lefort, Nathalie Vialaneix, Helene Quesnel, Marie–Christine Pere, Yvon Billon, et al (2020) Étude de la maturité des porcelets en fin de gestation par une approche métabolomique multifluide, 52. Journées de la Recherche Porcine, Feb 2020, Paris, France. IFIP – Institut du Porc – https://hal.archives-ouvertes.fr/hal-02479994/