Research Interests
Integrative omics for risk prediction and patient stratification
The interactions between human genetics, the microbiome and environment shape health and disease across life. We are applying integrative-omics of host and symbiotic microbes (genomics, transcriptomics, metabolomics, proteomics, epigenomics) to understand these complex interactions. Using multi-omics data from large-scale general population and patient cohorts, we focus on immune and metabolic changes with age to identify biomarkers and develop predictive models for patient stratification, personalized prevention and intervention strategies.
Genomic data-mining for functional understanding
The amount of information encoded in the microbial genomes is enormous, offering great opportunities to understand its function at greater resolution. We are developing bioinformatics frameworks, tools and databases to deeply mine multi-scale/multi-layer genetic data. More specifically, we gain insights into host−microbe interaction by investigating the interplay of microbial genetic variation (strain stratification, single nucleotide variations, structural variations, horizontal gene transfer) with human genome and health. This work facilitates our understanding of the functionality of the gut microbiome, its interactions with the human genome and exposome, as well as its dynamics over time and during disease development.
Mechanistic insights into host-microbe co-metabolism
Host−microbe interactions are heavily involved in metabolic diseases and inter-patient treatment responses. We are working to understand and recapitulate these critical interactions by developing personalized in vitro model systems that incorporate both host genetics and microbial communities To establish these models, we make use of stem cells, organ-on-a-chip technologies and culturomics. This multidisciplinary research aims to achieve mechanistic understanding that can facilitate the development of personalized medical approaches.
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