Network-based community analysis of microbial composition: Insights into dysbiosis and assembly

Traditional analyses of the human gut microbiome, often relying on differential abundance of individual taxa, face significant challenges due to the inherent complexity of microbial communities and frequently yield inconsistent findings. This study introduces a novel network-based approach to overcome these limitations by reducing data dimensionality and facilitating the identification of biologically relevant patterns. Our method constructs microbial association networks, applies community detection algorithms to identify robust groups of co-occurring species, and defines 'community strength variables' as the aggregated abundance of these communities, serving as a reduced-dimension framework for downstream analysis. We applied this approach to two distinct publicly available human gut microbiome datasets: a longitudinal study of infant gut microbiome assembly in Bangladeshi children and an obesity-control study in Danish adults. In the infant cohort, our analysis revealed key community transitions linked to developmental stages, highlighting the dynamic interplay between host growth and microbial colonization. For instance, Community 1 showed a strong positive correlation with age, while the Bifidobacterium-dominated Community 4 declined with age. In the Danish obesity study, our method identified distinct community profiles associated with obesity, effectively circumventing the inconsistencies observed in traditional differential abundance comparisons. Notably, Communities C1 and C19 showed significantly higher fractional abundances in lean participants compared to obese individuals. These findings underscore the context-specificity of microbial associations and demonstrate the power of community-level analysis. Despite limitations such as reliance on correlation-based networks, our framework offers a valuable tool for investigating microbiome structure and function, providing a promising avenue for developing robust microbiome-based biomarkers and therapeutic interventions through the quantification of key microbial consortia.