Holobiomics — integrative omics for holobionts

The HolobiomicsLab leverages state-of-the-art omics technologies — spanning genomics, proteomics, transcriptomics, and metabolomics — combined with advanced computational methodologies and AI integration to deliver a comprehensive, integrated view of complex biological systems. By capturing both the genetic blueprint and the dynamic expression of biomolecules, we unravel the multifaceted interactions within the holobiont, constructing detailed molecular portraits of host–microbiome symbiosis enhanced by machine learning and knowledge-graph technologies.

Research aims

Our mission is to unify diverse multi-layered datasets to elucidate the intricate molecular crosstalk between hosts and their associated microbiota. This integrative strategy, augmented by AI-driven data analytics and natural-language-processing tools, transcends the limitations of isolated omics analyses, deepening our understanding of cellular functions and the broader ecological dynamics that govern life — supporting innovations in ecosystem engineering, precision medicine, and sustainable agriculture.

Current projects

• Marine holobiont multi-omics. Investigating the molecular crosstalk between marine host organisms and their associated microbiomes. Marine holobionts — sea anemones and corals — serve as essential models for understanding how host–microbe interactions drive adaptation to environmental stress and underpin ecosystem resilience. In collaboration with Dr. Eric Rottinger (IRCAN, CNRS, Université Côte d'Azur), we integrate AI-assisted data interpretation with traditional omics analyses. Supported by a PhD fellowship from the Mission Interdisciplinaire pour les Initiatives Transverses (MITI CNRS, 2025, €120k).

Selected publications

• Shaffer, J. P., Nothias, L.-F., Thompson, L. R., et al. (2022). Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity. Nature Microbiology, 7, 2128–2150. 10.1038/s41564-022-01266-x
• Morton, J. T., Aksenov, A. A., Nothias, L.-F., et al. (2019). Learning representations of microbe–metabolite interactions. Nature Methods, 16, 1306–1314. 10.1038/s41592-019-0616-3