Prion-like proteins in marine plankton: a quest towards new molecular factors of thermal adaptation

Climate change is reshaping the distribution of species on the planet and mechanisms for adaptation to thermal stress are then called upon. Recently, in terrestrial plants the role of prion-like proteins has been highlighted in flowering and vernalization mechanisms. However, these atypical proteins have not been characterized in the marine world where plankton plays an essential role in the biological carbon pump and the marine food web. To explore the world of prion-like proteins and their role in thermal adaptation of marine plankton species, we propose a three years PhD program in the computational biology team of the CEA-SEPIA in the François Jacob Institute of Biology located in Fontenay-aux-Roses, France. The first objective of the thesis is to identify and characterize the function of marine prion-like proteins and their biogeography in the world oceans. The student will also reconstruct the molecular evolution of these proteins across a wide spectrum of marine plankton species through gain/loss and adaptation signal analyses. The research approach will be based on comparative genomics and phylogeny on Tara Oceans metagenomic and metatranscriptomic data. Additionally, the student will identify prion-like proteins involved in the adaptation to temperature by integrating spatial and environmental data collected by the Tara Oceans expeditions. In a context of climate change, this research fits into the understanding of the molecular evolution of prion-like proteins, shedding light on their role in the thermal adaptation of species playing a key role in the marine food chain and geochemical cycles.

Contribution of artificial intelligence (AI) to understand the modes of action of microRNAs, application to cancer

MicroRNAs have demonstrated importance in a large number of carcinogenesis processes ranging from initiation, propagation and the appearance of metastases. They raise many hopes as therapeutic treatment targets. However, the drug candidate MRX34 (which mimics a microRNA) proved to be a failure in patients because it was too toxic. It is therefore urgent to better understand the mode of action of microRNAs in order to design new therapeutic strategies.
The thesis project proposes to use two cutting-edge technologies for this: microRNA/mRNA co-sequencing data, at the single cell level, and artificial intelligence techniques (AI, including neural networks and XGBoost ). It will benefit from the contribution of two other projects, which end in 2025 (an overlap of a few months with the CFR thesis): a thesis financed by Pfizer-INSERM, and a multi-team project financed by the cancer plan. These two projects have already enabled rigorous statistical analysis of co-sequencing data at the single cell level, which will be used during the PhD work. A collaboration, already initiated, is planned with Gipsa-Lab, Grenoble, specialist in machine learning / AI.

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