Our Research
Research Focused on Reserve Polysaccharide Biology
The IBSP team (Integrative Biology of Storage Polysaccharides) conducts its research within the strategic axis ‘Biomass Polysaccharides’ led by the UGSF laboratory for the 2026–2030 period. Our work aims to elucidate the metabolism, structure, and regulation of storage polysaccharides, especially starch, to explore their evolutionary diversity and to understand their responses to abiotic stresses such as temperature and light.
This research takes on its full significance in the context of ecological, food, and energy transitions, where polysaccharides are expected to play a major role in food security, biomass valorization, and the development of sustainable biomaterials.
The work of the IBSP team is organized around four fundamental biological questions, which form the foundation of our scientific approach.
In parallel, we develop cross-cutting and exploratory projects, with a strong technological or interdisciplinary component, which enrich our understanding and open up new application perspectives.
1 / Initiation of Starch Synthesis
This process involves both the de novo synthesis of carbohydrate chains (priming) and their spatial organization (nucleation) to form a semi-crystalline granule. We study the proteins involved in these early stages. Our approach is based on a diversity of biological models, including
2 / Polysaccharide Phase Transition
The conversion of soluble glucans into semi-crystalline starch. This complex process involves debranching enzymes (DBEs), but also non-enzymatic proteins such as LESV and ESV1, whose functions remain partly unknown. We also study algal models such as Chromera velia, which exhibit alternative crystallization pathways without classical DBEs. This research combines Raman and FT-IR imaging, structural biochemistry, genome editing (CRISPR-Cas9), and single-starch-granule analysis to characterize the composition and fine organization of polysaccharides.
3 / Influence of Abiotic Stresses on Starch Metabolism.
Temperature is a major environmental signal, modulating photosynthesis, storage, and mobilization of carbon reserves. Our models, such as potato, pea, and certain marine microalgae, allow us to study phenomena such as “
4 / Role of Pyrenoidal Starch in Marine Photosymbioses
In certain microalgae, starch accumulates around the pyrenoid, a subcellular structure that concentrates CO2 around Rubisco. We study this particular organization using the
Our Cross-Cutting Projects
In parallel with these four biological questions, IBSP conducts a set of cross-cutting and interdisciplinary projects that enrich and extend these themes.
- We have notably developed a ‘single-granule omics’ project (ANR PRC SinGraMics), in collaboration with the MSAP laboratory, to analyze the protein and carbohydrate composition of individual starch granules.
- We also conduct metabolic engineering work in marine cyanobacteria within the framework of the ANR EPPIC project, to produce dextran-type polysaccharides under phototrophic conditions.
- Other projects aim to characterize protein–starch interactions, in partnership with the industrial laboratory Axomama (Florimond Desprez, Germicopa, SES Vanderhave), or to develop new technological tools.
These include:
– microalgae photoporation (PhLAM),
– non-invasive microwave sugar measurement (IEMN, IRCICA),
– encapsulation of therapeutic peptides in starch granules (IEMN),
– and advanced electrophoretic analysis methods for carbohydrate polymers (IPCM). - Cross-cutting projects are carried out in collaboration with the Advanced Spectroscopy Platform of the Chevreul Institute (Lille, FR2638) and LASIRE (U-Lille, UMR CNRS 8516).
- Vibrational micro-spectroscopy approaches (RAMAN or FT-IR) are developed and optimized on our models. Two major projects are thus explored: the diversity of microalgae (and among them, the identification of those that perform photosymbiosis), as well as the spectral characterization and chemical mapping of potato starch granules. All this data is then processed using multivariate statistical approaches (PCA, PLS-DA, sPLS-DA, hierarchical clustering..).
