Metabolite and species dynamics in microbial communities

Overview

Funded by EMBO for excellence in the life sciences.

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Course overview

Metabarcoding and metagenomic sequencing unveil the compositional diversity of microbial communities across habitats, from ocean ecosystems to the human gut. While compositional shifts often correlate with environmental or health states, mechanistic insights into dynamic microbiota-environment interactions are often lacking. Integrative approaches can shed light on these mechanisms in native and synthetic communities, such as metabolic cross-feeding driving subpopulation emergence (Varahan et al. eLife 2019, 2020) and long-term community stability (Blasche, Kim et al. Nat Microbiol 2021), or metabolic cues affecting environmental and gut microbiota structure (Clerc et al. Nat Commun 2023, Wuyts et al. Mol Sys Bio 2023, Adreani-Gerard et al. Env. Microbiome 2025, Deng et al. Nature Microbiology 2025, Kuhlish et al. PNAS 2026).

In the two previous editions, this course successfully equipped participants with tools to identify key interaction agents—metabolites—by assessing species and metabolite dynamics experimentally and using integrative approaches, such as genome-scale metabolic models, for interaction prediction. Reflecting advances in the field, for the 2026 edition, we are changing the model system from fermented milk (kefir) to synthetic marine microbial communities.

We will introduce the concepts of top-down and bottom-up approaches (e.g., large-scale metagenomic analysis of environmental samples and experimental design to mimic complex interactions in the lab using synthetic communities under controlled conditions), which are relevant to researchers investigating diverse microbial habitats.

Modules

• Experimental design, culture set-up, and sampling
• 16S analysis of bacterial communities using Nanopore sequencing technology
• Metabolite changes during community growth using Metabolomics
• Interaction mapping plating experiment (optional)
• Computational analysis introduction using R-studio/Python
• 16S and metabolomics data analysis (computational)
• Metabolic modelling of community interactions (computational)

Learning outcomes

The main goal of the course is to equip participants with state-of-the-art concepts in microbial ecosystems and to introduce key tools for gaining mechanistic insights into community dynamics. The central learning objective is for participants to apply these concepts and tools to their own research. Further, we also aim at enabling scientific networking among the participants (including speakers and trainers) and fostering dialogue between scientists from different areas (microbiology, metabolomics, modelling, ecology).

What past participants say about the course

“The course was a great blend of theoretical knowledge and practical applications. It was very dynamic, allowing for engaging discussions and networking opportunities among participants. From trainers to speakers and organisers, everyone was so helpful and approachable, making it easy to ask questions and seek guidance. The diverse facets and perspectives presented created an ideal environment for learning, hands-on exploration, and collaboration.” – Humberto Castillo González, Université de Franche-Comté, France.

Speakers

Scientific organisers

Sunil Laxman

Institute for Stem Cell Biology

and Regenerative Medicine

India

Course hub: Bangalore

Ian Probert

Sorbonne University

France

Course hub: Bangalore

Flora Vincent

EMBL Heidelberg

Germany

Course hub: Heidelberg

Maria Zimmermann-Kogadeeva

EMBL Heidelberg

Germany

Course hib: Heidelberg

Course organisers

Sophie Dutzi

EMBL Heidelberg

Germany

Course hub: Heidelberg

Michelle Steinhauer

EMBL Heidelberg

Germany

Course hub: Heidelberg

Preliminary programme

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Sponsors

Sponsorship opportunities

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