Individual participant data meta-analysis of metabolomics on sustained knee pain in primary osteoarthritis patients

Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies

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.

Machine learning-based endoplasmic reticulum-related diagnostic biomarker and immune microenvironment landscape for osteoarthritis

BACKGROUND

Osteoarthritis (OA) is the most common degenerative joint disease worldwide. Further improving the current limited understanding of osteoarthritis has positive clinical value.

METHODS

OA samples were collected from GEO database and endoplasmic reticulum related genes (ERRGs) were identified. The WGCNA network was further built to identify the crucial gene module. Based on the expression profiles of characteristic ERRGs, LASSO algorithm was used to select key factors according to the minimum λ value. Random forest (RF) algorithm was used to calculate the importance of ERRGs. Subsequently, overlapping genes based on LASSO and RF algorithms were identified as ERRGs-related diagnostic biomarkers. In addition, OA specimens were also collected and performed qRT-PCR quantitative analysis of selected ERRGs.

RESULTS

We identified four ERRGs associated with OA risk assessment through machine learning methods, and verified the abnormal expressions of these screened markers in OA patients through in vitro experiments. The influence of selected markers on OA immune infiltration was also evaluated.

CONCLUSIONS

Our results provide new evidence for the role of ER stress in the OA progression, as well as new markers and potential intervention targets for OA.

Culture-expanded mesenchymal stromal cell therapy: does it work in knee osteoarthritis? A pathway to clinical success

Osteoarthritis (OA) is a degenerative multifactorial disease with concomitant structural, inflammatory, and metabolic changes that fluctuate in a temporal and patient-specific manner. This complexity has contributed to refractory responses to various treatments. MSCs have shown promise as multimodal therapeutics in mitigating OA symptoms and disease progression. Here, we evaluated 15 randomized controlled clinical trials (RCTs) and 11 nonrandomized RCTs using culture-expanded MSCs in the treatment of knee OA, and we found net positive effects of MSCs on mitigating pain and symptoms (improving function in 12/15 RCTs relative to baseline and in 11/15 RCTs relative to control groups at study endpoints) and on cartilage protection and/or repair (18/21 clinical studies). We examined MSC dose, tissue of origin, and autologous vs. allogeneic origins as well as patient clinical phenotype, endotype, age, sex and level of OA severity as key parameters in parsing MSC clinical effectiveness. The relatively small sample size of 610 patients limited the drawing of definitive conclusions. Nonetheless, we noted trends toward moderate to higher doses of MSCs in select OA patient clinical phenotypes mitigating pain and leading to structural improvements or cartilage preservation. Evidence from preclinical studies is supportive of MSC anti-inflammatory and immunomodulatory effects, but additional investigations on immunomodulatory, chondroprotective and other clinical mechanisms of action are needed. We hypothesize that MSC basal immunomodulatory "fitness" correlates with OA treatment efficacy, but this hypothesis needs to be validated in future studies. We conclude with a roadmap articulating the need to match an OA patient subset defined by molecular endotype and clinical phenotype with basally immunomodulatory "fit" or engineered-to-be-fit-for-OA MSCs in well-designed, data-intensive clinical trials to advance the field.

The impact of omics research on our understanding of osteoarthritis and future treatments

PURPOSE OF REVIEW

To review recent studies using 'Omics' approaches (genomics, proteomics, metabolomics, single cell analyses) in patient populations and animal models of osteoarthritis (OA), with the goal of identifying disease-modifying mechanisms that could serve as therapeutic and diagnostic targets.

RECENT FINDINGS

The number of genes, pathways and molecules with potential roles in OA pathogenesis has grown substantially over the last 18 months. Studies have expanded from their traditional focus on cartilage and gene expression to other joint tissues, proteins and metabolites. Single cell approaches provide unprecedented resolution and exciting insights into the heterogeneity of cellular activities in OA. Functional validation and investigation of underlying mechanisms in animal models of OA, in particular genetically engineered mice, link Omics findings to pathophysiology and potential therapeutic applications.

SUMMARY

Although great progress has been made in the use of Omics approaches to OA, in both animal models and patient samples, much work remains to be done. In addition to filling gaps in data sets not yet existing, integration of data from the various approaches, mechanistic investigations, and linkage of Omics data to patient stratification remain significant challenges.