Advancing Health Data Exploitation through Secure Collaborative Learning
Recently, deep learning has been successfully applied in numerous domains and is increasingly being integrated into healthcare and clinical research. The ability to combine diverse data sources such as genomics and imaging enhances medical decision-making. Access to large and heterogeneous datasets is essential for improving model quality and predictive accuracy. Federated learning is currently developed to support this requirement offering an alternative by enabling decentralized model training while ensuring that raw data remains stored locally at the client side. Several open-source frameworks integrate secure computation protocols for federated learning but remains limited in its applicability to healthcare and raises issues related to data sovereignty. In this context, a French framework is currently developed by the CEA-LIST, introduces an edge-to-cloud federated learning architecture that incorporates end-to-end encryption, including fully homomorphic encryption (FHE) and resilience against adversarial threats. Through this framework, this project aims to deliver modular and secure federated learning components that foster further innovation in healthcare AI.
This project will focus on three core themes:
1) Deployment, monitoring and optimization of deep learning models within federated and decentralized learning solutions.
2) Integrating large models in collaborative learning.
3) Developing aggregation methods for non-IID situation.
Development of monoclonal antibodies for the diagnostic and the treatement of hypervirulent-Klebsiella pneumoniae
For several years, we have observed the emergence of hypervirulent (hvKp) strains of Klebsiella pneumoniae that have become highly resistant to antibiotics. In a context of dwindling antibiotic options, monoclonal antibodies (Abs) directed against well-conserved capsular antigens of these hvKp strains appear as a promising therapeutic alternative.
This PhD project is structured around three complementary objectives:
1. To describe the circulation of hvKp clones through comparative genomic analysis of strains collected via the French National Reference Center for Antibiotic Resistance and through an international collaboration.
2. To produce and characterize monoclonal Abs directed against the HvKp capsule.
3. To develop a rapid detection tool based on MALDI-TOF profile analysis coupled with machine learning algorithms.
Origins and evolution of prion-like proteins (PrLPs) in eukaryotes
Initially associated with neurodegenerative diseases, prion-like proteins (PrLPs) are now recognized as key physiological players in cellular plasticity and stress response. These proteins often contain an intrinsically disordered domain rich in glutamine and asparagine, known as a prion-like domain (PrLD), capable of switching between soluble, condensed, or amyloid states. Notable examples include CPEB in Aplysia, involved in synaptic memory, MAVS in antiviral defense, MED15 and FUS in transcriptional regulation and nucleocytoplasmic condensate dynamics, and ELF3 in plants, whose amyloid polymerization controls flowering and photoperiodic responses. In fungi, Sup35, Ure2p, and HET-s serve as experimental models of functional prions, demonstrating that reversible aggregation can act as a regulatory or adaptive mechanism. These conformational transitions are now viewed as adaptive molecular strategies rather than pathological anomalies.
This PhD project aims to trace the origin and diversification of prion-like proteins across eukaryotes, testing the hypothesis that major paleoclimatic crises have episodically promoted the emergence and duplication of genes encoding PrLDs through microsatellite expansion and transposable element activity. The project will combine large-scale phylogenomic analyses, PrLD domain detection, and modeling of selective pressures to map the key stages in the functional evolution of PrLPs and their links to stress tolerance.
The combined effects of hypoxia and matrix stiffness on the pathophysiology of pulmonary fibrosis.
Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fatal lung disease characterized by excessive extracellular matrix (ECM) deposition, increased tissue stiffness, and localized hypoxia. These alterations disrupt cell–cell interactions within the alveolo-capillary barrier and drive fibrotic progression. This project aims to investigate, under controlled in vitro conditions, the combined impact of mechanical stiffness and hypoxic stress on the fate and phenotype of pulmonary cell types and their intercellular communication. To achieve this, biomimetic polyacrylamide hydrogels with tunable stiffness and specific ECM protein coatings will be developed to support the co-culture of alveolar epithelial cells, endothelial cells, fibroblasts, and macrophages. Cellular responses will be assessed through proteomics, imaging, and secretome profiling. The goal is to identify key mechano- and chemo-dependent pro-fibrotic factors, providing new insights into IPF pathogenesis and opening avenues for targeted therapeutic strategies and lung tissue regeneration.
PtSeipin : linking lipid droplets biogenesis and degradation in the diatom Phaeodactylum tricornutum
Microalgae encompass a wide diversity of organisms and have attracted increasing interest due to their ability to produce biomolecules of biotechnological and industrial relevance. In particular, they can accumulate oil within lipid droplets (LDs) in response to abiotic stresses such as nitrogen deprivation. This oil accumulation holds great potential for biofuel production.We recently demonstrated that knockout of the gene encoding Seipin, a protein involved in LD biogenesis, leads to a strong oil accumulation in the diatom Phaeodactylum tricornutum. Moreover, this accumulation appears to result from an absence of LD degradation in the Seipin-deficient mutants. These findings suggest that, in this diatom, LDs are programmed to undergo degradation soon after their formation, thus inhibiting LD degradation could prove a promising strategy to increase their oil content.This project aims to elucidate the mechanisms underlying LD degradation and, more specifically, the connections between their biogenesis and degradation. Three main research axes will be pursued:
1. Identify PtSeipin interaction partners involved in LD degradation, using both candidate-based and unbiased approaches.
2. Characterize the LD degradation pathways disrupted in PtSeipin knockout mutants by combining electron microscopy with transcriptomic and proteomic analyses.
3. Investigate how microalgae utilize oil during the recovery phase after stress, through fluxomic approaches.
Regulation of gene expression by acetylation and lactylation of histone proteins
In eukaryotic cells, DNA wraps around histone proteins to form chromatin. Dynamic modification of histones by various chemical structures allows for fine regulation of gene expression. Alterations in these complex regulatory mechanisms are responsible for many diseases. Acetylation of histone lysines is known to induce gene expression. Other structures can be added to histones, whose effects on transcription remain largely unclear. Most of them, such as lactylation discovered in 2019, depend on cellular metabolism. We are studying this new modification in murine spermatogenesis: this process of cell differentiation is an ideal model for studying transcription regulation, due to dramatic changes in chromatin composition and gene expression patterns. We have established the distribution of acetylated and lactylated marks on three lysines of histone H3 across the genome. The aim of this thesis is to contribute to deciphering the “histone language,” first by studying the role of lactylations on the transcriptional program. Next, the prediction of chromatin states will be refined by integrating our new data with numerous available epigenomic data within neural network models.