Host-Microbiota Interactions and STI Outcomes

PROJECT SUMMARY To support the goals of the SIM-STI Program, that aims at developing an innovative biomimetic model of the cervicovaginal environment comprising the microbiota to elucidate how the host-microbiota interactions are driving host functioning and response to infection by C. trachomatis or N. gonorrhoeae. To study these interactions requires technical approaches that describe the system from a broad perspective without prior knowledge to observe functioning of both the host, the microbiome and the pathogen(s), such as global transcriptomics (human host RNAseq and bacterial community transcriptomics [metatranscriptomics]), glycomics and in-depth immune cytokines and chemokines characterizations. Here we propose to apply these omics approaches to support Project 1, 2 and 3 of the SIM-STI program. Further, transferring our proposed 3D biomimetic models from the engineering laboratory to three research laboratories for the study of sexually transmitted infections requires consistent biological resources to insure reproducibility and comparability. Leveraging the expertise and experience of its PI and infrastructure of the Institute for Genome Sciences at the University fo Maryland School of Medicine, the Omics/Clinical Core C will support this goal and will assemble and distribute biological resources (primary cell lines and reconstructed microbiota) using consistent protocols and qualitative evaluation of the materials to Project 1, 2 and 3, as well as the Biomimetic Models Core B. The overall goal of the Omics/Clinical Core C is to 1/ apply high-throughput omics approaches to samples provided by each of the project; 2/ provide microbiological (reconstructed microbiota) and primary cell purchase, isolation and maintenance services; 3/ perform a clinical study to collect cervicovaginal specimens (swabs, secretion and biopsies) for microbiota characterization, bacterial isolation and primary cell lines isolation; and 4/ disseminate the data and resources to the rest of the team and ultimately the scientific community through the Open Science Data Framework (OSDF), an innovative and scalable platform to store data of different types along with their metadata and create relationships between the different datasets.