Quantitative data study of Lipidomic Remodeling Following Ischemia-Reperfusion à Poitiers, France

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Le laboratoire Ischémie Reperfusion, Métabolisme et Inflammation Stérile en Transplantation (IRMETIST) est une unité mixte de recherche INSERM/Université de  Poitiers (U1313). Elle est située à Poitiers sur le site du CHU à proximité de la faculté de Médecine et de Pharmacie.

Les thématiques de recherche de l’unité s’articulent autour de l’amélioration des conditions de la transplantation d’organes, principalement le rein et le foie, et autour de l’étude des phénomènes lésionnels liées à l’ischémie-reperfusion (IR).

Cette équipe monothématique est dirigée par le Professeur Luc Pellerin.

Description

Mission

In a context of liver grafts shortage for transplantation on the one hand, and increasing numbers of people suffering from metabolic diseases metabolic dysfunction-associated liver disease (MASLD) on the other, this project proposes to study the impact of ischemia-reperfusion (IR) injury on lipid metabolism in healthy and steatotic livers, and to develop pharmacological strategies to prevent the metabolic disturbances that have been observed.

To this end, we developed an in vitro and in vivo model of steatotic livers undergoing IR. The first one is based on differentiated HepaRG cells that were lipid-loaded and subjected to a hypoxia/reoxygenation sequence. The second model consists of steatosis induced by a Western diet; these mice then undergo hepatic pedicle clamping followed by declamping. From these different matrices (cells, liver, plasma), lipids were extracted, separated by liquid chromatography, and then analyzed by LC-MS/MS using an Orbitrap.

We are currently looking for a motivated candidate to process lipidomic data using a comprehensive bioinformatics pipeline to ensure robust identification and quantification. Data will be normalized to protein content, followed by peak alignment and intensity correction (using internal standards) with Compound Discoverer®. Then, data exploration will be performed using both unsupervised and supervised methodologies to identify lipid species that contribute most to the discrimination between experimental groups. Clustering, heatmaps and dimension reduction methodologies will be especially used to better discriminate the lipids and understand their intensities. Many analysis will be conducted on this study. We will firstly focus on exploratory analysis (descriptive, bivariate, multivariate, …). We will also conduct mecanistic study (pathway analysis, pathway enrichment, differential analysis, functional analysis,…). The significance of individual variable contribution will be assessed using parametric (e.g. t-test or ANOVA) or non-parametric (Rank Tests) when normality and variance equality are not attained, with p-values adjustment using the Benjamini-Hochberg False Discovery Rate (FDR) procedure due to the large size of the dataset. All analyses will be conducted using R with a significance threshold set at p <0.05.

The objectives of this internship are therefore to:

1) Identify lipid species that contribute the most to the discrimination between experimental groups (variables hypoxia/ischemia and/or steatosis)

2) Identify potential lipid biomarkers that can predict future lesions

3) Build user interfaces that are easy for non-bioinformatician colleagues to use

Profil

Level: Enrollment in a Master 2 program on applied mathematics, statistics, biostatistics, Data Science or bio-informatics

Required skills:

• Knowledge in statistical modeling and adjustment methods

• Proficient in R programming or Python.

• Scientific rigor and critical thinking

• Writing and communication skills

Prise de fonction

04/01/2027