Peripheral blood mononuclear cell epigenetic associations in and biomarkers for knee osteoarthritis development and progression

Project Summary / Abstract The objective of the proposed research is to better understand how peripheral blood immune cell composition and/or epigenetic patterns change during knee osteoarthritis (OA) development and progression. Our laboratory has previously examined in detail the biomarker potential of peripheral blood epigenetic changes both to predict future rapid radiographic/pain progression in patients who already have early knee OA, as well as to detect preclinical OA (e.g. patients who do not have radiographic OA but will develop it in the near future). In the current study, we will expand upon these findings both to generate higher-resolution epigenetic maps of peripheral blood as well as to determine epigenetic changes within individual immune cell subsets. Our first Aim is to perform high-resolution sequencing-based 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) analysis on OA patients from across the disease spectrum. Our second Aim is to determine epigenetic changes (and correlated gene transcription changes) within individual immune cell subsets using an innovative paired single-cell RNA- seq and ATAC-seq analysis pipeline and compare these findings with those of Aim 1. Our third Aim is to develop a novel set of high-resolution DNA methylation array-based peripheral blood composition models using scRNA- seq data from Aim 2 and additional data already generated by our institution, and then to apply these models to our previously-generated large OA peripheral blood DNA methylation datasets, thereby enabling the detection of small changes in immune cell subsets in various OA patient endotypes. The proposed work is important, as we do not have a full understanding of how the systemic immune system influence OA risk, nor do we have robust clincally-available biomarkers to distinguish future OA progressors or detect preclinical disease. Our work is quite innovative in its hypotheses as well as the use of cutting-edge deep single-cell and deep sequencing epigenetic analysis and use of machine learning methods to develop discriminatory models. Success in our proposal may open a new avenue for OA inflammation research and may offer a novel treatment strategy for OA.