Viral community diversity in the rhizosphere of the foundation salt marsh plant Spartina alterniflora

Abstract

Viruses of microorganisms impact microbial population dynamics, community structure, nutrient cycling, gene transfer, and genomic innovation. In wetlands, root-associated microbial communities mediate key biogeochemical processes important for plants involved in ecosystem maintenance. Nonetheless, the presence and role of microbial viruses in salt marshes remains poorly understood. In this study, we analyzed 24 metagenomes retrieved from the root zone of Spartina alterniflora, a foundation plant in salt marshes of the eastern and Gulf coasts of the U.S. The samples span three plant compartments--bulk sediment, rhizosphere, and root--and two cordgrass plant phenotypes: short and tall. We observed differentiation between phenotypes and increased similarity in viral communities between the root and rhizosphere, indicating that plant compartment and phenotype shape viral community composition. The majority of viral populations characterized are novel at the genus level, with a subset predicted to target microorganisms known to carry out key biogeochemical functions. The findings provide a holistic assessment of plant-associated viral diversity and community composition as well as identifying potential targets for exploring viral modulation of microbially-mediated ecosystem functioning in intertidal wetlands. ImportanceSalt marshes are vital coastal ecosystems. Microbes in these environments drive nutrient cycling and support plant health, with Spartina alterniflora serving as a foundation species. This study explores viral communities associated with S. alterniflora, revealing how plant compartment and phenotype shape viral composition. The discovery of numerous novel viruses, some potentially influencing microbes involved in key biogeochemical processes, highlights their ecological significance. Given the increasing pressures on coastal ecosystems, understanding virus-microbe-plant interactions is essential for predicting and managing ecosystem responses to environmental change.