Arthritis (AR) is the leading cause of disability in the United States, with 50 million Americans (22%) suffering from some form of the disease or chronic joint symptoms. The CDC estimates that, by the year 2030, 67 million adults will have AR and the economic burden will reach an annual cost of $128 billion dollars. While most AR cases are classified as osteoarthritis (OA), affecting over 27 million Americans, other forms of the disease are also prevalent, including rheumatoid arthritis, juvenile arthritis and gout. AR is associated with the breakdown of collagen and the production of inflammation-related factors such as cytokines. The degradation of Type I and Type II collagens in AR patients results in the presence of detectable peptide biomarkers in urine;these include CTX-II, CTX-I and C2C. The levels of these biomarkers in urine track with the extent of cartilage damage in patients. The Osteoarthritis Biomarkers Network has classified these biomarkers regarding their applicability to OA diagnosis, prognosis and efficacy of treatment. Because AR can progress slowly, and often patients with cartilage degeneration are asymptomatic for years, diagnosing AR in its early stages and monitoring its progression has been a major challenge. We propose to develop a non-invasive, highly sensitive diagnostic assay for AR using DNA origami molecules that can detect these three biomarkers. In Phase I, we will evaluate the feasibility of performing DNA origami selections against the CTX-II, CTX-I and C2C peptides to identify DNA sequences that we will develop as probes in an assay to specifically diagnose AR.
Three aims are proposed:
AIM1 : Design and construct a DNA origami library for AR biomarker selections.
AIM 2 : Isolate DNA origami molecules that interact with AR biomarker peptides.
AIM 3 : Identify and characterize DNA origami molecules that bind the AR biomarkers. Due to the DNA origami molecular structure, the interaction between these molecules and the AR biomarkers should exhibit increased binding affinities, which contributes significantly to the sensitivity and ingenuty of this diagnostic assay. This assay will provide the medical community with a reliable, non-invasive test for AR that offers increased sensitivity and convenience relative to current assays. Following successful completion of our Phase I aims, this work will continue into Phase II, with the development of a label-free, highly sensitive detection assay, using silicon nanowires to signal the presence of these biomarkers in urine. The development of a non-invasive multiplex diagnostic assay for AR has the potential to result in a clinical paradigm shift from therapies tha are largely palliative to one that focuses on identifying persons with early stage AR. The assay will also have a prognostic and treatment efficacy value for monitoring cartilage breakdown in patients receiving various treatments. Further, this assay will provide the necessary surveillance and an excellent vehicle to motivate afflicted individuals to make lifestyle-changing decisions (e.g. weight loss) to increase their quality of life. Lastly, if the feasibility of the method is demonstrated, the method could be applied to other candidate biomarkers in AR.

Public Health Relevance

Arthritis (AR) is a debilitating, slowly developing disease that makes diagnosis and the assessment of intervention effectiveness difficult. Our proposed project to develop a user-friendly, highly sensitive and label- free diagnostic assay for the detection of multiple AR biomarkers in patients suffering with AR will fulfill an unmet clinical need for reliable diagnostic assays capable of identifying early arthritis. This project has high overall impact for clinicians and other health professionals as earlier diagnosis of AR and will help inform prognosis, monitoring and therapeutic strategies.

National Institute of Health (NIH)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
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Special Emphasis Panel (ZRG1-MOSS-S (10))
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Wang, Xibin
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Lynntech, Inc.
College Station
United States
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