19Viruses and bacteria are critical components of the human microbiome and play important roles in health and 20 disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing 21 them to two separate communities. Such approaches are unable to capture how these microbial communities 22 interact, such as through processes that maintain community robustness or allow phage-host populations 23 to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network 24 diversity throughout the human body. We built these community networks using a machine learning algorithm 25 to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to 26 paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We 27 organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness 28 across the human body. We observed evidence that gut and skin network structures were person-specific 29 and not conserved among cohabitating family members. High-fat diets appeared to be associated with 30 less connected networks. Network structure differed between skin sites, with those exposed to the external
Author Summary
37The human microbiome, the collection of microbial communities that colonize the human body, is a crucial 38 component to health and disease. Two major components to the human microbiome are the bacterial and 39 viral communities. These communities have primarily been studied separately using metrics of community 40 composition and diversity. These approaches have failed to capture the complex dynamics of interacting 41 bacteria and phage communities, which frequently share genetic information and work together to maintain 42 ecosystem homestatsis (e.g. kill-the-winner dynamics). Removal of bacteria or phage can disrupt or even 43 collapse those ecosystems. Relationship-based network approaches allow us to capture this interaction 44 information. Using this network-based approach with three independent human cohorts, we were able to 45 present an initial understanding of how phage-bacteria networks differ throughout the human body, so as to 46 provide a baseline for future studies of how and why microbiome networks differ in disease states.