PROJECT SUMMARY Significance: Childhood respiratory diseases pose a significant health burden and lack primary prevention strategies. With genetics explaining only a fraction of the risk, the highly adaptable human microbiome is of particular interest. Multiple studies have linked asthma and allergy to the early-life gut and nasal microbiome, yet results are inconsistent and current studies have significant limitations. Thus, despite this great promise, the clinical potential of the microbiome remains unfulfilled: robust microbiome-based asthma predictors and targets for primary prevention are yet to be developed, and potential mechanisms underlying its involvement remain elusive. Further, breastfeeding, a key determinant of infant microbiome development, is associated with reduced childhood asthma rates, yet the intricate relationships between breastfeeding, microbiota development, and respiratory health remain poorly understood. Here, I propose to combine state-of-the-art algorithms with large longitudinal studies and animal models to elucidate the intricate mechanisms through which breastfeeding and microbial colonization modulates early-life respiratory health outcomes. Approach: This grant will leverage two established birth cohorts (CHILD and COPSAC; > 14,000 samples 16S, shotgun metagenomic and metabolomics) to investigate early-life microbiomes, across two microbial niches, alongside breastfeeding characteristics, human milk components and detailed clinical outcomes from birth to early adolescent. We will first develop an unsupervised machine-learning tool for dimensionality reduction of longitudinal multi-omics data to identify robust colonization patterns across microbial niches (Aim 1). Next, we will identify microbial genetic adaptation and colonization patterns underlying childhood respiratory diseases. We will use these to detect specific microbial functions and genetic regions associated with childhood respiratory diseases, facilitating an investigation of potential mechanisms (Aim 2). Finally, we will determine the role of breastfeeding and breastmilk as regulators of microbiome-asthma interactions. We will develop a causal inference framework alongside an animal model of weaning and examine the microbial and immune response to early weaning as well as lung resistance (Aim 3). Innovation: This study represents a paradigm shift in research through several key innovations: (1) a dynamic approach capturing microbial communities over time for more robust and reproducible results; (2) simultaneous modeling of nasal/airway and gut microbiomes across large birth cohorts, revealing shared patterns; (3) metagenomic sequencing, growth rates, structural variations, and selection metrics for population genetic insights; (4) integration of human milk composition analysis; (5) a novel early weaning model; and (6) state-of- the-art computational methods for precise multi-omics time-series analysis. Overall, by uniting large longitudinal datasets...