Job offer

Organisation/Company: INSERM U1082

Research Field: Medical sciences

Researcher Profile: Recognised Researcher (R2), Leading Researcher (R4), First Stage Researcher (R1), Established Researcher (R3)

Country: France

Application Deadline: 15 May 2025 - 22:00 (UTC)

Type of Contract: Temporary

Job Status: Full-time

Offer Starting Date: 1 Sep 2025

Is the job funded through the EU Research Framework Programme? Not funded by a EU programme

Is the Job related to staff position within a Research Infrastructure? No

Offer Description

- Problem, Context and Scientific Objectives / Societal Issues
This project targets organ deficiency, by proposing to go back upstream of the donor-organ-recipient route by developing quantitative and reproducible solutions for donor selection.

Currently, donors and their organs are evaluated qualitatively by team members, resulting in variability that means many organs are not used, although it is established that at least 2/3 of them could have been successfully transplanted. This implies that each year more than 200 patients waiting for a kidney transplant are not transplanted, and for patients waiting for hearts, lungs, and livers, there are 100, 400, and 200 organs respectively that are not used, dramatically impacting patients.

Our objectives are to determine and validate new biomarkers as well as formulate an algorithm for determining the quality of grafts using machine learning.

- Positioning in relation to the state of the art in the field and justification of relevance
Kidney transplantation presents major challenges, particularly regarding the shortage of organs. Demand for transplants far exceeds supply (4,354 transplants for 10,990 patients waiting in 2022). Donations must be optimized, but this will remain impossible as long as the capacity to quantify the quality of an organ is not established.

Currently, the only donor qualification score is the Kidney Donor Profile Index (KDPI), which has moderate performance in its initial cohort (c statistics of 0.6) and is unreliable in a European cohort, making the discovery of new biomarkers essential.

- Relevance of the methodological approach in the scientific context
The biological complexity of brain death, coupled with the multifactorial aspect of ischemia-reperfusion that the organ undergoes between harvest and transplantation, requires the use of wide-field (open-ended) omics analyses. We propose the use of Metabolomics, the large-scale study of small molecules, commonly called metabolites. This approach is powerful because the metabolites and their concentrations directly reflect the biochemical activity and therefore the molecular phenotype.

- Methodology:
- Patient cohort: The collection of biological samples (blood and urine) from organ donor patients who died of brain death began in 2017 and now includes 110 sets of samples. This cohort is supplemented by a biobank from the Nantes University Hospital, comprising 70 donors, making it the largest organ donor biobank available for biomarker research.

- Metabolomics: A plasma extract is injected into the liquid chromatography - high resolution mass spectroscopy (LC-HRMS) system, according to 4 successive programs, combining two column technologies (C18 and HILIC) as well as two types of ionization (negative or positive). The acquisition is carried out on an Orbitrap Exploris 120 (Thermo Fisher). This protocol is the result of extensive optimization and has been shown to produce the best quality and quantity of data.

- Analysis of the signal by Artificial Intelligence:
The goal will be to produce an algorithm that can estimate the quality of the organ, equating it to the function of the organ once transplanted in the short and long term. We will build a machine learning model that predicts the quality of the organ based on data from omics tools.

- Preliminary data: We conducted a preliminary metabolomics study on a sub-cohort of donors generating hundreds of signals that we explored using machine learning. Our results show good performance (area under the ROC curve of 0.75) of an algorithm predicting organ function 3 months after transplantation, demonstrating the potential for omics.

- Share of innovation and socio-economic impact:
Innovation: As our preliminary data show, metabolomics and transcriptomics can identify molecules that provide information on organ quality. These targets offer new opportunities for the development of therapeutics and biomarkers.

Socio-economic impact: An algorithm for determining the quality of the organ will allow the healthcare team to implement more advanced preservation procedures, or even ex-vivo repair, to better match the organ and the recipient, anticipating post-transplant complications, substantially increasing the patient's quality of healthy life and reducing healthcare costs.

- Thesis co-financing information:
We request a ½ thesis grant from the Nouvelle Aquitaine region, under the co-direction of Raphael Thuillier (metabolomics, machine learning), Thomas Kerforne (resuscitation, transplantation), and Carlos Prieto (machine learning), at the U1313 IRMETIST laboratory (director: Luc Pellerin) and the Universidad de Salamanca, Servicio de Bioinformática. We have secured the first half of the grant with the university of Poitiers.

- Integration of the “open science” component into the project
The results of this project will initially remain confidential to allow evaluation, in collaboration with the University of Poitiers, of their patentability. Once this security has been achieved, our approach will be in accordance with the 'open science' policy: publications will be made available online, and databases as well as source codes will be shared on public platforms. The doctoral student will be encouraged to participate in popularization initiatives aimed at the general public.

Minimum Qualifications: Master 2 in a relevant discipline: Data Science or similar.