In our work we combine sequencing and imaging approaches with computational and statistical analysis techniques to study the composition, function and spatial organisation of the human microbiome. To be able to study the complex microbe-host crosstalk in the human holobiont, we develop experimental techniques (sequencing and imaging protocols) and computational tools (taxonomic and functional profiling software as well as statistical and machine learning analysis workflows). With this toolbox we characterize dysbiotic and eubiotic community constellations in relation to complex host phenotypic or molecular profiles (e.g. dietary habits, host metabolomes or transcriptomes).
Focusing on specific disease areas, primarily cancers and neurodegenerative diseases, we conduct meta-analyses to derive robust microbiome signatures as a basis for evaluating their potential as diagnostic or prognostic biomarkers. By investing microbial gene function using computational inference tools to mine metagenomic data, we aim to gain an understanding of the mechanisms underlying these associations and propose testable mechanistic hypotheses.
We finally study microbiome-diet interactions and how microbiome modulation can be combined with dietary interventions to optimize metabolic health and modify disease risk. To achieve these goals, we collaborate across disciplines with microbiologists, immunologists, data scientists and clinicians. We embrace open science principles to promote reproducible (computational) research.
