Our multidisciplinary research group employs a comprehensive, multi-layered approach to decode the complex relationship between the microbiome – microbes living in and on organisms and their by-products – and the host’s aging. It’s becoming increasingly clear that the microbiome plays an important role in aging. Many studies now points to the profound impact microbial compositions have on healthspan and longevity. The shifts in gut microbiome composition are associated with age-related diseases and other degenerative conditions. Additionally, experiments involving microbiota transfers between young and older laboratory organisms have demonstrated influences on lifespan and age-associated declines in motor functions, cognition, and immune responses. By employing different molecular profiling techniques such as RNAseq, single cell RNAseq, and microbiome profiling (using 16S amplicon sequencing and shotgun metagenomics), combined with expertise in data analysis and machine learning, we study age-related dynamics of microbes, how the interaction between microbes and host changes with age, and the potential of these changes to predict health outcomes during aging. Our ultimate goal is to understand the roles of individual microbes and their interactions in promoting healthy aging. Furthermore, we aim to determine how these microbial components may interact with other potential aging interventions and lifestyle factors, paving the way for microbiome-centric therapeutics. In this presentation, I will provide insights into our current projects. These include examining the microbiome’s role in brain aging and neurodegeneration, investigating the phenomenon of a ‘leaky gut’ — where microbes breach the intestinal wall to enter the bloodstream — and predicting the host’s aging using microbiome data. Overall, the microbiome holds the potential to serve as a dynamic mirror reflecting the aging process and environmental influences while also holding the power to influence the host. As we continue exploring this complex interaction scape, we hope to leverage advances in machine learning and data analytics to develop microbiome-based tools with diagnostic and therapeutic potential.