Projects in our lab are focused on using cutting-edge high-throughput technologies and novel computational approaches to better understand immune development and responsiveness. Ongoing projects include:

Integrative Analysis of the Gut Microbiome’s Role in Immune Aging:

Decline and dysregulation of immune function is a major underlying cause of morbidity and mortality in old age. While many cellular and functional age-associated changes in the immune system have been described, the molecular networks which regulate immune aging have not been elucidated. Furthermore, the causes of many age-related immune dysfunctions such as chronic elevation of inflammatory responses are also not well understood. Despite being one of the most prominent sources of immune stimulation in our body, the relationship between the gut microbiota and immune aging remains relatively unexplored. The composition and structure of the microbiome is known to change with age, including decreases in the phylum Firmicutes and overall diversity, but the mechanisms by which such changes can contribute to the processes of immunosenescence and chronic inflammation is unknown. In this project we are performing an integrative analysis of DNA/RNA sequencing, metabolomics, and flow cytometry measurements of the microbiome and immune system in a cohort of young (18-45) and older (>65) adults to generate a systems-level understanding of the relationship between age-related changes in the gut microbiome and immune aging.

Transcriptional Atlas of Human Immune Responses to 13 Vaccines:

Systems biology approaches have been used to define molecular signatures and mechanisms of immunity to vaccination. However, most such studies have been performed on single vaccines, and comparative analysis of the response to different vaccines is lacking. Using publicly available datasets, we are performing a meta-analysis of temporal transcriptional data of over 3,000 samples, obtained from 820 adults across 28 studies of 13 different vaccines, to understand how variation in vaccine platforms (inactivated, live attenuated, viral vector, etc.), target pathogens and adjuvants can influence the immune response. We are also using machine learning approaches to identify early transcriptional signatures predictive of the magnitude and durability of antibody responses across multiple vaccines, in order to guide development of more effective and long-lasting vaccine formulations.

A graph of the overlap in upregulated transcriptional modules across vaccine responses on day one post-vaccination.

Overlap in upregulated transcriptional modules across vaccine responses on day one post-vaccination.