Research

What we do 🧪 🔬 🧬

At HolobiomicsLab, we integrate advanced analytical/computational chemistry and artificial intelligence to decode the intricate metabolic interplay between hosts and their associated microbiota (aka holobionts). Our interdisciplinary approach spans from deep metabolomics to AI-driven data analysis, enabling breakthrough discoveries in environmental science, microbial ecology, and holobiont biology/engineering.

Research

Deep Metabolomics for Environmental and Holobiont Studies

We leverage advanced analytical chemistry and artificial intelligence to decode the intricate metabolic interplay between hosts and their associated microbiota. Our approach enables the sensitive detection of low-abundance metabolites, including microbially derived metabolites that are inaccessible by conventional methods. By integrating robotics, chromatographic and mass spectrometric technologies, and coupling these with computational methods for instrument control and mass spectra annotation, we generate comprehensive and highly detailed molecular profiles of environmental and holobiont systems.

Research Aims 🎯

Our mission is to illuminate the metabolic and functional dynamics 🔆 within complex environmental and holobiont communities, driving novel therapeutic and ecological breakthroughs. Through identification of key molecular interactions and elucidation of microbial communication mechanisms that underpin host biology and environmental adaptation, we are supporting discoveries with potential transformative applications in precision medicine, agronomy, pollution monitoring, and ecosystem resilience enhancement 🌱.

Current Projects 🚀

Supported by the Junior Chair Professor (2023–2027) from the CNRS Chemistry, our research combines state-of-the-art mass spectrometry acquisition with advanced computational methods to provide a holistic view of molecular landscapes in marine holobionts. Focusing on sea anemone models—representative of cnidarians such as corals 🪸—and their responses to environmental stresses, the initiative is designed to unravel the underlying molecular regulation. Through this integrated experimental and computational approach, we aim to advance understanding of both environmental processes and holobiont biology, contributing to a new paradigm in integrative omics research. This chair is co-funded by the CNRS (200k€) and the IDEX of Université Côte d'Azur (120k€).

Lucas Pradi's doctoral project focuses on developing an innovative, automated mass spectrometry acquisition framework 🤖 for Orbitrap instruments. The main aim is to enhance the quality and coverage of fragmentation spectra—overcoming the limitations of conventional acquisition strategies—while designing a user-friendly solution that democratizes advanced metabolomics analysis and deepens our understanding of complex biological systems. This project is funded by the FNR Luxembourg (200k€, 2024-2028).

Selected Publications and Resources 📚

Hoffmann, M. A., Nothias, L.-F., Ludwig, M., et al. (2022). High-confidence structural annotation of metabolites absent from spectral libraries. Nature Biotechnology, 40, 411-421. https://doi.org/10.1038/s41587-021-01045-9

Dührkop, K., Nothias, L.-F., Fleischauer, M., et al. (2021). Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nature Biotechnology, 39, 462-471. https://doi.org/10.1038/s41587-020-0740-8

Zuo, Z., Cao, L., Nothias, L.-F., & Mohimani, H. (2021). MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition. Bioinformatics, 37(Suppl\_1), i231-i236. https://doi.org/10.1093/bioinformatics/btab279

Research

Next Generation Mass Spectrometry for Microbial Metabolomics 🔬 🧫 ⚡

We employ state-of-the-art instrumentation 🔭 and specialized expertise to conduct high-throughput mass spectrometry metabolomics across thousands of microbial extracts. Our approach integrates customized analytical pipelines with advanced pre-enrichment techniques, optimized chromatography, and innovative hybrid mass spectrometry acquisitions—including emerging ionization technologies—to enhance detection of specific metabolite classes. We further incorporate single-cell isolation technologies 🦠 to expand our access to microbial metabolic potential, ensuring comprehensive exploration of microbial diversity and functionality.

Research Aims 🎯

By combining cutting-edge technologies with poly-targeted strategies 🎯 for monitoring select molecular classes, we maximize both sensitivity and data interpretability. Our commitment to continuous analytical and computational refinement enables us to push detection and identification limits in high-throughput settings. Our ultimate goal is to deliver the most advanced analytical pipeline for rapid microbial metabolome characterization ⚡, thereby revealing the complex metabolic networks that underpin microbial functions and interactions within diverse environments.

Current Projects 🚀

The "DeciPhering the rOle of PolyamineS in Bacterial Virulence" project (funded by ANR-POPS ANR-24-CE44-1190, 193k€, 2023-2027) investigates how polyamines modulate bacterial virulence in Pseudomonas aeruginosa and Staphylococcus aureus 🦠. By combining controlled modifications of polyamine biosynthetic pathways with integrated omics approaches, we aim to uncover the molecular mechanisms driving pathogenicity. The HolobiomicsLab is developing advanced metabolomics approaches for profiling and interpreting the metabolome of Staphylococcus aureus when exposed to polyamines.

The "Microbiomics" project (co-funded by the Academie 4 of IDEX Université Côte d'Azur, 2023, 50k€) establishes a high-throughput experimental integrative omics pipeline 🧪. By combining innovative single-cell dispensing with rapid advanced metabolomics and state-of-the-art computational approaches, we enable accelerated, automated generation of comprehensive microbial libraries. This strategy transforms the detection and annotation of microbially produced specialized metabolites, deepening our understanding of microbial functions within holobionts and environmental systems.

Relevant Publications 📚

Nothias, L.-F., Schmid, R., Garlet, A., Cameron, H., Leoty-Okombi, S., André-Frei, V., Fuchs, R., Dorrestein, P. C., & Ternes, P. (2024). Functional metabolomics of the human scalp: a metabolic niche for Staphylococcus epidermidis. mSystems, 9(2), e0035623. https://doi.org/10.1128/msystems.00356-23

Kunyavskaya, O., Tagirdzhanov, A. M., Caraballo-Rodríguez, A. M., Nothias, L.-F., Dorrestein, P. C., Korobeynikov, A., Mohimani, H., & Gurevich, A. (2021). Nerpa: A Tool for Discovering Biosynthetic Gene Clusters of Bacterial Nonribosomal Peptides. Metabolites, 11(10), 693. https://doi.org/10.3390/metabo11100693

Cao, L., Gurevich, A., Alexander, K. L., et al. (2019). MetaMiner: A Scalable Peptidogenomics Approach for Discovery of Ribosomal Peptide Natural Products with Blind Modifications from Microbial Communities. Cell Systems, 9(6), 600-608.e4. https://doi.org/10.1016/j.cels.2019.09.004

Research

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. Through this approach, we aim to reveal the fundamental regulatory networks and adaptive processes underlying holobiont biology, supporting innovations in ecosystem engineering, precision medicine, and sustainable agriculture 🌱.

Current Projects 🚀

Our current multi-omics research investigates the molecular crosstalk between marine host organisms and their associated microbiomes 🌊. Marine holobionts—such as sea anemones and corals 🪸—serve as essential models for understanding how host-microbe interactions drive adaptation to environmental stresses 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 to reveal the fundamental regulatory networks governing symbiosis and functional plasticity. This approach informs strategies to mitigate climate change impacts on vulnerable marine ecosystems, demonstrating our commitment to advancing interdisciplinary science that generates innovative insights and practical applications in holobiont biology, with significant implications for environmental restoration 🌱. This research is supported by a PhD fellowship from the Mission Initiative Transverse et Interdisciplinaire du CNRS (2025, 120k€).

Relevant Publications 📚

Shaffer, J.P., Nothias, LF., Thompson, L.R. et al. Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity. Nat Microbiol 7, 2128–2150 (2022). https://doi.org/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. https://doi.org/10.1038/s41592-019-0616-3

More work in progress...

Research

AI Systems for Accelerating Metabolomics and Integrative Omics Research 🤖 🧠 💻

Our approach centers on adopting and developing AI-driven tools 🤖 that streamline both mass spectrometry data acquisition and interpretation in metabolomics and related omics fields. By training foundational mass spectrometry models and leveraging large language models, we are designing AI systems capable of autonomously operating and optimizing our mass spectrometers 🔬, interfacing with next-generation human-computer interaction platforms. Concurrently, we are developing AI-assisted literature mining methods 📚 to enhance experimental planning and facilitate data interpretation. This integrated approach improves the precision of molecular insights and enables rapid, robust elucidation of the biochemical underpinnings of complex biological systems.

Research Aims 🎯

We are committed to creating intuitive, open AI-powered frameworks 🧠 that expedite molecular discovery and foster data-driven insights. By harnessing state-of-the-art computational advances—including natural language processing for hypothesis generation, dynamic visualization tools, and interactive analytical frameworks 📊—our primary goal is to transform the interpretation of metabolomics data through an open-access metabolomics AI assistant 🤖. Our solutions are designed with flexibility to ensure seamless extension to integrative multi-omics applications. We aim to accelerate the cycle of hypothesis testing and validation, ultimately paving the way for breakthroughs in understanding metabolic regulation and systems biology.

Current Projects 🚀

The MetaboLinkAI project, "Open, Integrative, and Extendable Artificial Intelligence and Knowledge Graphs Framework for Functional and Actionable Metabolomics," is an international research consortium initiative (2025–2029) co-funded by the Swiss National Science Foundation (SNF 10002786, 3.16M€) and the French Agence Nationale de la Recherche (ANR-24-CE93-0012-01, 1.3M€). Dedicated to transforming the collection, management, and interpretation of metabolomics data through artificial intelligence 🧠, it is coordinated by ETH Zurich and CNRS. The initiative unites eight leading scientific institutions from France (including Inria and INRAE) and Switzerland (such as UNIGE, UNIFR, UZH, and SIB). By merging deep expertise in metabolomics, knowledge engineering, and AI, MetaboLinkAI is committed to fostering open science and building a global community of researchers and innovators. Along with Prof. Nicola Zamboni (ETH Zurich), the HolobiomicsLab is co-coordinator of this international project and leads the development of the metabolomics AI assistant 🤖. More information is available at www.metabolinkai.net.

The "KGbot: Knowledge Graph Chatbot for Metabolomics" project develops an AI system that leverages language models to translate natural language queries into structured data requests over knowledge graphs 🔍. This innovative approach, prototyped in chemistry and metabolomics, facilitates interactive exploration of complex datasets—thereby accelerating hypothesis generation and refining metabolite annotation. The project is funded by Academie 1 IDEX Université Côte d'Azur (70k€, 2024–2025) and supported by a part-time software engineer from the Interdisciplinary Institute for Artificial Intelligence (3iA) Côte d'Azur (2024). More information is available at ds4h.univ-cotedazur.fr/recherche/projets-finances/projet-kg-bot.

The "Deep Reinforcement Learning for Mass Spectrometry Data Acquisition in Metabolomics" project, conducted by Madina Bekbergenova's for her doctoral research, aims to optimize real-time mass spectrometry data acquisition 🔬. By employing offline reinforcement learning to dynamically adjust instrument configurations, the project maximizes the quality and coverage of fragmentation spectra. This innovative approach promises to uncover metabolites that are currently overlooked, significantly enhancing molecular discovery in complex biological samples. This project is part of an international PhD program with Prof. Wout Bittremieux, funded by the EUR Spectrum of the Université Côte d'Azur and 3iA Côte d'Azur (2024-2027, 120k€).

Relevant Publications 📚

Tysinger, E., Pagni, M., Kirchhoffer, O., Mehl, F., Gandon, F., Wolfender, J.-L., & Nothias, L.-F. (2023). An Artificial Intelligence Agent for Navigating Knowledge Graph Experimental Metabolomics Data. 2023 Swiss Metabolomics Society Annual Meeting, Swiss Metabolomics Society Zurich; ETH Zurich, September 2023, Zurich, Switzerland. https://hal.science/hal-04381448/

Schmid, R., Petras, D., Nothias, LF. et al. Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment. Nat Commun 12, 3832 (2021). https://doi.org/10.1038/s41467-021-23953-9

Nothias, L.-F., Petras, D., Schmid, R., et al. (2020). Feature-based molecular networking in the GNPS analysis environment. Nature Methods, 17, 905-908. https://doi.org/10.1038/s41592-020-0933-6

More work in progress ...

Research

Code Repository and Tools

Open Source Software

See our code repositories at https://github.com/holobiomicslab

Checkout our public web-apps:

🧪 MetaboT 🍵: metabot.holobiomicslab.cnrs.fr
🔬 ScienceGuide 🧭: metaboguide.holobiomicslab.cnrs.fr

Research

Scientific Communities 🌐 👥 🔗

Main Scientific Communities

MetaboLinkAI Community

An international initiative fostering collaboration in AI-driven metabolomics research beyond the core consortium partners. This growing community invites researchers, industry experts, and technology innovators worldwide to contribute to developing open frameworks for metabolomics data interpretation. The initiative embraces open science principles and prioritizes knowledge sharing over siloed competition, creating opportunities for broader participation in addressing complex challenges in metabolomics and other omics fields through artificial intelligence approaches. See www.metabolinkai.net.

Interdisciplinary Institute for Artificial Intelligence (3IA) Côte d'Azur

As an active participant in this prestigious institute, we contribute to France's AI innovation ecosystem. Established in 2019 and led by Université Côte d'Azur with support from major research institutions and over 62 companies, 3IA Côte d'Azur develops real-world AI applications, particularly in health domains. Our involvement enhances our computational capabilities and connects our research to broader AI initiatives. See 3ia.univ-cotedazur.eu/.

Metabolomics Society

An international organization dedicated to advancing metabolomics through scientific conferences, publications, and educational initiatives. See metabolomicssociety.org/.

RFMF (Réseau Français de Métabolomique et Fluxomique)

We maintain strong ties with this French metabolomics and fluxomics network, collaborating with national colleagues to advance metabolomics research and applications in France. See www.rfmf.fr/.

American Society for Mass Spectrometry (ASMS)

Our team regularly engages with this community through conference participation and knowledge exchange, benefiting from the latest developments in mass spectrometry technologies and applications. See www.asms.org/.

MZmine Community

An open-source platform for mass spectrometry data processing that provides a flexible framework supporting various MS data types, including LC-MS, GC-MS, and MS imaging. The community actively develops and maintains this tool, enhancing data processing capabilities through collaborative software development and commitment to open science principles. See mzio.io/mzmine-news/.

Global Natural Products Social Molecular Networking (GNPS)

A web-based mass spectrometry ecosystem designed for sharing and analyzing tandem mass spectrometry data. GNPS enables molecular networking, spectral library searches, and collaborative curation, connecting researchers worldwide and advancing metabolomics research capabilities through its comprehensive platform and tools. See gnps.ucsd.edu.