Browsing by Author "Rice, Sarah J."

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Evolution and advancements in genomics and epigenomics in OA research: How far we have come
(Elsevier BV, 2024-02) Ramos, Yolande F. M.; Rice, Sarah J.; Ali, Shabana Amanda; Pastrello, Chiara; Jurisica, Igor; Farooq Rai, Muhammad; Collins, Kelsey H.; Lang, Annemarie; Maerz, Tristan; Geurts, Jeroen; Ruiz Romero, Cristina; June, Ronald K.; Appleton, C. Thomas; Rockel, Jason S.; Kapoor, Mohit
Objective. Osteoarthritis (OA) is the most prevalent musculoskeletal disease affecting articulating joint tissues, resulting in local and systemic changes that contribute to increased pain and reduced function. Diverse technological advancements have culminated in the advent of high throughput “omic” technologies, enabling identification of comprehensive changes in molecular mediators associated with the disease. Amongst these technologies, genomics and epigenomics – including methylomics and miRNomics, have emerged as important tools to aid our biological understanding of disease. Design. In this narrative review, we selected articles discussing advancements and applications of these technologies to OA biology and pathology. We discuss how genomics, deoxyribonucleic acid (DNA) methylomics, and miRNomics have uncovered disease-related molecular markers in the local and systemic tissues or fluids of OA patients. Results. Genomics investigations into the genetic links of OA, including using genome-wide association studies, have evolved to identify 100+ genetic susceptibility markers of OA. Epigenomic investigations of gene methylation status have identified the importance of methylation to OA-related catabolic gene expression. Furthermore, miRNomic studies have identified key microRNA signatures in various tissues and fluids related to OA disease. Conclusions. Sharing of standardized, well-annotated omic datasets in curated repositories will be key to enhancing statistical power to detect smaller and targetable changes in the biological signatures underlying OA pathogenesis. Additionally, continued technological developments and analysis methods, including using computational molecular and regulatory networks, are likely to facilitate improved detection of disease-relevant targets, in-turn, supporting precision medicine approaches and new treatment strategies for OA.
Three Decades of Advancements in Osteoarthritis Research: Insights from Transcriptomic, Proteomic, and Metabolomic Studies
(Elsevier BV, 2023-12) Farooq Rai, Muhammad; Collins, Kelsey H.; Lang, Annemarie; Maerz, Tristan; Geurts, Jeroen; Ruiz-Romero, Cristina; June, Ronald K.; Ramos, Yolande; Rice, Sarah J.; Ali, Shabana Amanda; Pastrello, Chiara; Jurisica, Igor; Appleton, C. Thomas; Rockel, Jason S.; Kapoor, Mohit
Objective. Osteoarthritis (OA) is a complex disease involving contributions from both local joint tissues and systemic sources. Patient characteristics, encompassing sociodemographic and clinical variables, are intricately linked with OA rendering its understanding challenging. Technological advancements have allowed for a comprehensive analysis of transcripts, proteomes and metabolomes in OA tissues/fluids through omic analyses. The objective of this review is to highlight the advancements achieved by omic studies in enhancing our understanding of OA pathogenesis over the last three decades. Design. We conducted an extensive literature search focusing on transcriptomics, proteomics and metabolomics within the context of OA. Specifically, we explore how these technologies have identified individual transcripts, proteins, and metabolites, as well as distinctive endotype signatures from various body tissues or fluids of OA patients, including insights at the single-cell level, to advance our understanding of this highly complex disease. Results. Omic studies reveal the description of numerous individual molecules and molecular patterns within OA-associated tissues and fluids. This includes the identification of specific cell (sub)types and associated pathways that contribute to disease mechanisms. However, there remains a necessity to further advance these technologies to delineate the spatial organization of cellular subtypes and molecular patterns within OA-afflicted tissues. Conclusions. Leveraging a multi-omics approach that integrates datasets from diverse molecular detection technologies, combined with patients’ clinical and sociodemographic features, and molecular and regulatory networks, holds promise for identifying unique patient endophenotypes. This holistic approach can illuminate the heterogeneity among OA patients and, in turn, facilitate the development of tailored therapeutic interventions.