Next generation sequencing discovers known and novel circulating micrornas in knee osteoarthritis

Abstract

Purpose: There are no biomarkers that can be used in a therapeutic, prognostic, or diagnostic manner for osteoarthritis (OA). Circulating microRNAs have emerged as promising biomarkers for a variety of diseases. MicroRNAs are small non-coding RNAs that repress expression of target genes. Our lab was the first to perform global array screening of microRNAs in OA, but this approach identifies only known microRNAs. The aim of this study is to use next generation sequencing to identify novel circulating microRNAs in patients with OA. This technology has never been applied to identifying circulating microRNAs in OA, and has the sensitivity and specificity to detect microRNAs that are unique to various cohorts, including early OA versus late OA, men versus women, young versus old, and normal weight versus overweight. These data will be used to establish biomarker signatures which distinguish cohorts of OA patients. Methods: Plasma samples from OA patients are obtained from the Knee Osteoarthritis BioBank at the University Health Network in Toronto, Canada. Cohorts are defined based on Kellgren-Lawrence radiographic grading for early OA (grades 0 & 1) and late OA (grades 3 & 4). Patients with comorbidities that might affect microRNA signatures are excluded. Plasma samples from 10 normal donors, 10 early OA patients, and 10 late OA patients will be subjected to next generation sequencing of microRNAs. Data analysis will be performed in R 3.2.2 using the edgeR package (v3.18.1). Candidate microRNAs will be selected for validation by real-time PCR in additional cohorts of early OA (N=100), late OA (N=100), and normal donors (N=100). Biostatistic methods will be used to identify relationships between microRNA signatures and known risk factors such as sex, age, and body mass index. Bioinformatic methods will be used to predict the targets and biological function of candidate microRNAs in OA. Results: Initial sequencing analysis of 5 normal donors and 5 late OA patients is complete. A total of 2579 known and 59 novel microRNAs were identified. These were filtered for microRNAs with at least 10 counts per million of classified sequences in at least 2 of 10 samples, resulting in 314 known and 15 novel microRNAs. From this, a list of top 20 differentially expressed microRNAs was generated based on false discovery rate < 0.05, log counts per million > 2, log fold change > 1.5, and p-value < 0.0003. Hierarchical clustering of these microRNAs revealed a distinct pattern between normal and late OA samples. Among these microRNAs is a candidate previously shown to be dysregulated in OA synovial fluid, and a novel putative microRNA that has not previously been identified. Validation of these findings by real-time PCR and additional sequencing experiments are currently underway. Conclusions: Interim results suggest that next generation sequencing is a useful approach for identifying known and novel microRNAs in OA. Sequencing of early OA samples will allow identification of microRNA signatures that can be used to distinguish these patients from late OA patients. Circulating microRNAs may represent valid and reliable biomarkers with potential applications for improving OA detection and treatment.