Upper respiratory-genetic and nasal microbiome risk factors for rheumatoid arthritis - PROJECT SUMMARY/ABSTRACT The applicant is a rheumatologist at Mayo Clinic whose long-term goal is to become an independent investigator uncovering the biologic pathways of rheumatoid arthritis (RA) onset to improve its diagnosis and treatment. This proposal describes a focused plan for Dr. Kronzer to achieve that goal by acquiring the research training and experience necessary to transform from an epidemiologist to a patient-oriented translational scientist with genomic and microbiome expertise. How RA develops remains unknown, hindering the early diagnosis and treatment needed to reduce its morbidity and mortality. The applicant recently found that upper respiratory diseases increase RA risk. Therefore, there is a critical need to define the biologic pathways linking inflammation in the upper respiratory tract with RA risk. The overall objective of this application is to identify the strongest upper respiratory-genetic and nasal microbiome risk factors for RA. The central hypothesis is that the upper respiratory tract interacts with genetics and the microbiome to drive RA risk. The rationale is that the upper respiratory tract has a strong association with RA and is easily accessible, making it an ideal site for developing novel clinical tools. To test the central hypothesis, the specific aims of this proposal are to identify (1) upper respiratory disease-gene interactions and (2) nasal microbiome signatures of RA risk. For Aim 1, supervised machine learning models will incorporate the top upper respiratory disease- gene interactions obtained from the Mayo Clinic Biobank, Mayo Clinic Tapestry Study, Mass General Brigham Biobank, and All of Us (combined n > 565,000 with genetic data, 7,199 with RA). For Aim 2, 60 new-onset, untreated RA cases and 60 household controls will provide nasal swabs for shotgun metagenomic sequencing followed by machine learning analytics. To accomplish both aims, mentors Drs. Crowson (primary), Sparks (co-primary), Cerhan, and Walther-Antonio will provide senior mentorship alongside an outstanding mentorship network. The proposed work is innovative because it brings (1) upper respiratory tract, (2) machine learning, (3) larger sample sizes, and (4) new-onset, validated RA cases to the study of genomics and the microbiome in RA. Upon successful completion of the proposed research, the expected outcomes are novel upper respiratory disease-gene interactions (Aim 1) and microbiome signatures (Aim 2) for RA, providing much-needed insights into how upper respiratory inflammation leads to RA. In addition, the applicant will gain expertise in machine learning, genomics, patient-oriented research, and microbiome/bioinformatics. Together, these research and training outcomes are expected to uniquely position Dr. Kronzer to submit a competitive R01 application on the nasal host/bacterial transcriptome and point-of-care algorithms predicting RA diagnosis and treatment response. Ultimately, these outcomes are expected to have a positive impact on earlier diagnosis and personalized treatment selection needed to improve morbidity and mortality for individuals with RA.
