Jaeyun Sung, PHD

The focus of human gut microbiome science is rapidly moving from descriptive analytics to predictive biomedical applications.

To this point, we ask: What comprises a ‘healthy’ gut microbiome? How do we measure and test for this? And can its routine monitoring provide actionable information to optimize health and well-being?

Finding answers to such clinically relevant questions using large-scale microbiome data drives one prominent research direction in our laboratory. I will present one notable example, wherein we recently developed the Gut Microbiome Health Index (GMHI), a mathematical formula that determines the degree to which a gut microbiome profile reflects the presence (or absence) of disease independent of the clinical diagnosis (Gupta et al. Nat Comm, 2020).

GMHI was formulated based on 50 gut microbial species associated with health, which were identified through a multi-study pooled analysis on 4,347 human stool metagenomes from 34 published studies across healthy and twelve different disease conditions.

When demonstrated on our population-scale meta-dataset, GMHI was found as the most robust and consistent predictor of general health compared to α-diversity indices commonly considered as markers for gut health. Validation of GMHI on 679 samples from nine additional studies resulted in remarkable reproducibility in distinguishing healthy and non-healthy groups.

Our findings highlight the potential of GMHI to inform dietary choices and other interventions for maintaining or restoring health through gut microbiome modulation. Ultimately, we expect our work to be one cornerstone for a suite of new, omics data-based clinical tools under development in our lab to aid the early detection, diagnosis, prognosis, and treatment monitoring of complex chronic diseases.

Jaeyun Sung, Ph.D., develops computational analytical approaches to understand the intricate relationship between commensal microbial organisms and human health. Of particular interest to Dr. Sung is characterizing the global interconnectivity within our body's microbial community, as well as identifying the primary biomolecular processes driving microbiome-associated chronic disease.

Dr. Sung's work is conducted primarily through network modeling and systems-level, integrative analyses of multiomics data from clinical samples. Ultimately, Dr. Sung aims to apply insights from his data-driven research to inspire future investigations into human microbiome biocomplexity and to create novel technologies that can lead to breakthroughs in improving human health.