We investigate the global and temporal changes of human microbiome in health and diseases. We develop computational and quantitative systems biology approaches to understand the role of microbiome in host physiology and disease pathophysiology to propose an improved microbiome-based personalised treatment and diagnosis for future therapy.
Our projects in the lab fall in these categories:
Construction of Human Microbiome/Mycobiome Catalogues and Metagenome Species
Metagenomic studies on the human gut microbiome has enabled the characterization of the microbial and functional diversity in health and diseases. Advances in metagenome assembling and various clustering methods have enabled the generation of co-abundance gene groups (CAGs) and metagenome species, which has allowed for generation of thousands of Metagenome-Assembled Genomes (MAGs) and Metagenome Species Pan-genomes (MSPs). In several different projects in the lab, we update and develop different microbial gene catalogues together with metagenome species, including bacteria, fungi and phage to achieve better representation of uncultured genomes in the human microbiome.
Functional and Compositional Changes of Human Microbiome in Health and Disease
As microbiota known to affect host physiology, there are increasing demands to understand microbial mechanism and underlying functions. Here we develop advanced bioinformatics tools and databases to enable us to explore functional repertoire of many microorganisms. We characterize the generated metagenome species and gene catalogues with several functional annotations such antimicrobial resistance, virulence factors, mobile genetic elements, and secondary metabolites. We then apply this information as well to understand the distinct and shared microbial functions on different body sites for several diseases. Our recent human gut microbiome atlas (https://www.microbiomeatlas.org) is the example of outcomes from this theme of projects.
Quantitative Systems Biology and AI in Host-Microbiome Interactions
In different studies, we analyse multi-omics data on host and microbiome and we integrate these data in a personalized fashion using different biological network systems, where different data on transcriptomics, metabolomics and metagenomics can be integrate together and also can be linked to immunome. The outcomes will also lead to a personalized approach and better stratification of patients based on multi-dimensional data. These findings will also provide valuable information to perform personalized predictive modelling to simulate and coupling treatment with host-microbiome interactions and therefore suggest an improved microbiome-based personalized medicine pathway for therapy.