AUTHOR=Liberti Assunta , Natarajan Ojas , Atkinson Celine Grace F. , Sordino Paolo , Dishaw Larry J. TITLE=Reflections on the Use of an Invertebrate Chordate Model System for Studies of Gut Microbial Immune Interactions JOURNAL=Frontiers in Immunology VOLUME=12 YEAR=2021 URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.642687 DOI=10.3389/fimmu.2021.642687 ISSN=1664-3224 ABSTRACT=

The functional ecology of the gastrointestinal tract impacts host physiology, and its dysregulation is at the center of various diseases. The immune system, and specifically innate immunity, plays a fundamental role in modulating the interface of host and microbes in the gut. While humans remain a primary focus of research in this field, the use of diverse model systems help inform us of the fundamental principles legislating homeostasis in the gut. Invertebrates, which lack vertebrate-style adaptive immunity, can help define conserved features of innate immunity that shape the gut ecosystem. In this context, we previously proposed the use of a marine invertebrate, the protochordate Ciona robusta, as a novel tractable model system for studies of host-microbiome interactions. Significant progress, reviewed herein, has been made to fulfill that vision. We examine and review discoveries from Ciona that include roles for a secreted immune effector interacting with elements of the microbiota, as well as chitin-rich mucus lining the gut epithelium, the gut-associated microbiome of adults, and the establishment of a large catalog of cultured isolates with which juveniles can be colonized. Also discussed is the establishment of methods to rear the animals germ-free, an essential technology for dissecting the symbiotic interactions at play. As the foundation is now set to extend these studies into the future, broadening our comprehension of how host effectors shape the ecology of these microbial communities in ways that establish and maintain homeostasis will require full utilization of “multi-omics” approaches to merge computational sciences, modeling, and experimental biology in hypothesis-driven investigations.