The Zeller team develops analysis strategies and tools to investigate how the microbiome contributes to human health, disease progression and treatment success, and how it is shaped by host factors such as nutrition and drug intake.

Previous and current research

The human microbiome, the complex ecosystem of microorganisms colonising our body, has increasingly been recognised as an important determinant of human physiology. Detailed investigations of microbes in situ (without culturing) have become possible through advances in sequencing technology and computational analysis methodology. These have now started to be applied in large clinical studies to associate changes in microbiome composition and function with human diseases. However, analysis and interpretation of such data remains challenging:

  • Quantifying microbial (sub-)species and functions in an accurate manner consistently across various sequencing readouts (16S, shotgun metagenomics and metatranscriptomics) is still difficult for complex communities consisting of many uncultured organisms.
  • Microbiome data interpretation is often complicated by many factors that vary in addition to the phenomenon of interest; typical confounders include differences in lifestyle, comorbidities or treatments. Comparisons across studies (meta-analyses) are hampered by batch effects arising from technical variation in sample preservation and preparation.
  • Perturbations of the microbiome are poorly understood to date. Systematic data and predictive models on the specific effects of environmental exposures (such as host-targeted drugs) on the microbiome are lacking despite this being a key aspect of personalised health and a potential entry point for designing intervention strategies targeted at the microbiome.

To address these challenges, we are actively contributing to the development of software tools for accurate profiling of both previously sequenced and uncharacterised microbial species, and the functions encoded in their genomes and transcriptomes. To associate changes in these profiles with various host phenotypes of interest, we have investigated various statistics and machine learning tools and evaluated their applicability to microbiome sequencing data and are currently making software pipelines publicly available that automate such analyses. Using these, we have recently demonstrated that gastrointestinal diseases can be accurately detected from faecal microbiome readouts. For colorectal cancer in particular this has potential for developing novel non-invasive screening methods (see figure).

Figure 1: Colorectal cancer (CRC) can be detected using a classification approach based on microbial markers (top panel) quantified in faecal samples by metagenomic sequencing.

Figure 1: Colorectal cancer (CRC) can be detected using a classification approach based on microbial markers (top panel) quantified in faecal samples by metagenomic sequencing; its accuracy was evaluated in cross-validation and independent external validation (bottom panels) in comparison to the standard non-invasive screening test (FOBT Hemoccult).

Future projects and goals

  • Development of statistical analysis tools for microbiome–host association studies that are tailored to the specifics of metagenomics data, leverage longitudinal study designs, handle confounding in a principled way, and enable cross-study comparisons (meta-analysis).
  • Integrative analysis of 16S, metagenomics, metatranscriptomics, and metabolomics data with the goal of associating these microbiome read-outs to molecular profiles of host health states.
  • A better understanding of the roles the gut microbiome plays in disease development, drug metabolism, and treatment outcome (e.g. in the context of cancer and organ transplantation) in collaboration with the ZimmermannTypas, and Bork groups, as well as with clinical partners.