Scientific Research Identifying Biomarkers and Interacting Pathways for Rheumatoid Arthritis
The lives of my family and friends have profoundly influenced my aspirations and interests. Their sufferings and hardships have galvanized my curiosity into genomics and bioinformatics. Inspired by my family member’s fight with Rheumatoid Arthritis (RA) and the need to find specific and effective early-stage treatments for the disease, I pursued independent research. I utilized bioinformatics tools to establish more effective synovial-tissue-based diagnostics for RA. I have firsthand witnessed the debilitating effects of RA in daily functioning. My mother can’t successfully conduct simple tasks like opening a jar of jam or sitting criss-cross on the ground during RA flare-ups. Climbing stairs, running, exercising, and exertion above a certain threshold become increasingly challenging as Rheumatoid Arthritis continues its attack on synovial tissue that lines the joints. I completed this work with the hope to potentially alleviate my mother’s symptoms and experiences with RA.
Rheumatoid Arthritis (RA) is a clinically heterogeneous and complex autoimmune disease that affects approximately 1.2 million Americans and 20 million people worldwide. According to the World Health Organization (WHO), within ten years of onset, at least 50% of patients in developed countries cannot hold down a full-time job, presumably due to the disabilities that ensue with RA. RA is also known to have a cascading effect in terms of the overall deterioration of health. RA increases the risk of heart disease as it causes inflammation throughout the body and can lead to an attack of the pericardium. It can also lead to obesity as pain in joints can lead to a decrease in exercise. Uncontrolled inflammation due to RA can cause destruction and wear down cartilage, leading to joint deformities and eventual bone erosion. It is advantageous to diagnose RA before extensive erosion as treatments are more effective at early stages. However, this is relatively difficult as RA’s early-stage symptoms usually include subtle symptoms like fatigue, slight fever, and stiffness, which are not specific enough for RA as opposed to apparent symptoms. Although a number of RA treatments have been found, a significant number of patients still fail to respond to current medication due to the nonspecificity of the drugs. While the mechanistic reason for such failure rates remains unknown, the cellular and molecular signatures in the synovial tissues of RA patients are likely to play a role in the variable treatment response and heterogeneous clinical evolution. Currently, blood-based criteria are employed, but such serologic parameters do not necessarily reflect biological actions in the target tissue of the patient and are relatively nonspecific to RA. These blood-based targets not only associate with RA but are also linked to various other similar diseases, such as osteoarthritis, hence demonstrating the nonspecificity of these targets. Synovial-tissue-based biomarkers are especially attractive as they can provide a confirmed diagnosis for RA. Currently, there is still a shortage of accurate synovial tissue-based identifiers for RA diagnosis. Thus comes the need for this study.
Some of the current medicines that treat RA include Humira, Plaquenil, and Methotrexate. Although these medications are effective to a certain extent, they include severe side effects and are not very effective at inhibiting RA. Methotrexate, in particular, can cause serious, life-threatening side effects and should only be taken in severe conditions. Various drugs, especially those that fall into the Disease-modifying antirheumatic drugs (DMARDs) category treat just the symptoms of the disease and don’t attack the root cause. Thus, a drug based on a particular gene or network target may have a significant effect not just on the current patient but also in stopping the genetic transmission of disease-causing agents.
Microarray data and gene expression studies are also subject to variation, with discrepancies between studies because of differences in methodology, sample characteristics, etc. For example, the storage method and age of tissue samples affect the contained RNA. My work examined three different microarray studies. I utilized the limma package in R to find common differentially expressed genes in all three datasets as well as genes in only two out of three datasets. By analyzing only the common genes and removing the not common genes allowed for a more accurate depiction of the biological mechanism underlying RA.
Understanding the programming, getting credible advice, and productively understanding advanced concepts were the key difficulties that I encountered during this project. For this project, I utilized various packages in R. As I did not have previous experience with this language, I spent a considerable amount of time learning how to write multiple analysis techniques in R Studio. I had trouble getting in contact with experts to discuss my findings, but luckily with sustained efforts, I was able to gain advice from my teachers, computational biology researchers, and professionals. As a lot of the methodologies and concepts used in this project’s analysis were based on advanced mathematics and biological concepts, I spent a substantial amount of time understanding the reasoning behind the procedures used. My strong background in mathematics and biology was instrumental in helping me understand the concepts applied in this project. My magnet school’s rigorous curriculum provided me the opportunity to explore advanced concepts such as linear algebra, multivariable calculus, biotechnology, organic chemistry, and computer science. My extracurricular involvement in biotechnology and molecular genetics in Science Olympiad further enriched my knowledge in the respective fields.
Overall, this research plays an important role in determining novel biomarkers that warrant further study for therapeutic development and diagnostic capabilities. The development of biomarkers is one of the clinical methods of early diagnosis of many diseases. Although looking at single genes might have a local focus, looking at the novel gene network identified will allow for a focal point of data and criteria that correlates with disease development.
RA is a common autoimmune disease that is often difficult to diagnose at early stages due to the lack of specific symptoms. A synovial tissue-based gene biomarker approach is beneficial in diagnosing patients earlier as well as developing more targeted, efficacious treatments. The novel biomarkers and pathways identified in this paper are potential criteria to be used in diagnostic tests to target therapeutics and drugs. The specificity of this study serves as a significant improvement that should be leveraged by scientists to develop specific pathomechanism tests and drugs for Rheumatoid Arthritis.
Through this hands-on research endeavor, I have gained remarkable and unique experience in the computational biology subfield of systems biology. I have expanded my interests from molecular genetics and biotechnology to integrate computation, mathematics, and biology.
