2017.06.12报告题目：Exploring the human microbiome in health and disease
报告人：丁涛, Ph.D. New York University博士后
An initial goal of the Human Microbiome Project (HMP), funded by the National Institutes of Health in the United States, was to provide a microbial profile of each site across the healthy human body, allowing association studies between the microbiome and potential changes in health. Using that data, I proposed the concept of community types and found that while there existed considerable intra- and interpersonal variation in the human microbiome, this variation could be partitioned into community types that were predictive of each other and the result of life-history characteristics. In more recent studies, I have been exploring community types and the dynamics of the microbiome in response to acute respiratory infections, such as influenza. Severe cases of influenza infection are often associated with secondary bacterial co-infections with opportunistic pathogens, such as Staphylococcus aureus, Streptococcus pneumoniae and Streptococcus pyogenes. To understand the role of the respiratory microbiome in determining disease severity in influenza, we characterized the microbiota in respiratory samples from infected hosts. Our analysis of specimens collected from infected ferrets—the animal model in influenza—shows that there was drastic modulation of the respiratory microbiota over the course of the infection, with significant differences in community structures before infection in ferrets that developed severe disease compared to those with mild disease. This indicates that the microbiota can potentially be predictive of disease severity. Similarly, with patient specimens from the oropharynx we observed that having been vaccinated against influenza could significantly alter the composition of the microbiota such that during influenza infection, vaccinated patients harbored fewer pathogenic bacterial strains than unvaccinated patients. These studies provide new insight into how the microbiome contributes to disease severity in both chronic conditions and in acute infections, and will lead to better modeling of disease risk and to personalized therapies.