Understanding the dynamics and adaptive mechanisms of the human gut microbiome across geography and time is critical for linking microbial composition to host health. In this project, we will analyze longitudinal whole-genome shotgun (WGS) metagenomic data from the Exposome dataset, consisting of 35 Swedish individuals sampled at four time points, to explore microbiome variation and potential environmental influences. In parallel, we will process gut microbial data from 6026 US individuals from the NHANES dataset, enabling cross-cohort comparisons between population-scale microbiomes. Additionally, we will profile another longitudinal Swedish cohort (the Wellness cohort) comprising 75 individuals, also sampled at four time points (totaling 300 samples), to investigate intra-individual microbiome stability and shared adaptive features across Swedish cohorts. Combined, these datasets provide a powerful framework to assess temporal dynamics, population-specific microbial traits, and potential microbial biomarkers. We will employ standardized metagenomic pipelines for quality control, taxonomic and functional profiling, and downstream statistical modeling. Machine learning methods will also be applied to identify microbial signatures and adaptive traits linked to host or environmental variables. A small compute (20,000 conputing hours) and medium storage (10TB) allocation is requested to support these analyses.