Objectives: To present an unbiased approach to identify positional transcript single nucleotide polymorphisms (SNPs) of osteoarthritis (OA) risk loci by allelic expression imbalance (AEI) analyses... Show moreObjectives: To present an unbiased approach to identify positional transcript single nucleotide polymorphisms (SNPs) of osteoarthritis (OA) risk loci by allelic expression imbalance (AEI) analyses using RNA sequencing of articular cartilage and subchondral bone from OA patients. Methods: RNA sequencing from 65 articular cartilage and 24 subchondral bone from OA patients was used for AEI analysis. AEI was determined for all genes present in the 100 regions reported by the genome-wide association studies (GWAS) catalog that were also expressed in cartilage or bone. The count fraction of the alternative allele (phi) was calculated for each heterozygous individual with the risk SNP or with the SNP in linkage disequilibrium (LD) with it (r(2) > 0.6). Furthermore, a meta-analysis was performed to generate a meta-phi (null hypothesis median phi = 0.49) and P-value for each SNP. Results: We identified 30 transcript SNPs (28 in cartilage and two in subchondral bone) subject to AEI in 29 genes. Notably, 10 transcript SNPs were located in genes not previously reported in the GWAS catalog, including two long intergenic non-coding RNAs (lincRNAs), MALAT1 (meta-phi = 0.54, FDR = 1.7x10(-4)) and ILF3-DT (meta-phi = 0.6, FDR = 1.75x10(-5)). Moreover, 12 drugs were interacting with seven genes displaying AEI, of which seven drugs have been already approved. Conclusions: By prioritizing proxy transcript SNPs that mark AEI in cartilage and/or subchondral bone at loci harbouring GWAS signals, we present an unbiased approach to identify the most likely functional OA risk-SNP and gene. We identified 10 new potential OA risk genes ready for further translation towards underlying biological mechanisms. Show less
Almeida, R.C. de; Mahfouz, A.; Mei, H.L.; Houtman, E.; Hollander, W. den; Soul, J.; ... ; Meulenbelt, I. 2021
Objective. To identify OA subtypes based on cartilage transcriptomic data in cartilage tissue and characterize their underlying pathophysiological processes and/or clinically relevant... Show moreObjective. To identify OA subtypes based on cartilage transcriptomic data in cartilage tissue and characterize their underlying pathophysiological processes and/or clinically relevant characteristics.Methods. This study includes n = 66 primary OA patients (41 knees and 25 hips), who underwent a joint replacement surgery, from which macroscopically unaffected (preserved, n = 56) and lesioned (n = 45) OA articular cartilage were collected [Research Arthritis and Articular Cartilage (RAAK) study]. Unsupervised hierarchical clustering analysis on preserved cartilage transcriptome followed by clinical data integration was performed. Protein-protein interaction (PPI) followed by pathway enrichment analysis were done for genes significant differentially expressed between subgroups with interactions in the PPI network.Results. Analysis of preserved samples (n = 56) resulted in two OA subtypes with n = 41 (cluster A) and n = 15 (cluster B) patients. The transcriptomic profile of cluster B cartilage, relative to cluster A (DE-AB genes) showed among others a pronounced upregulation of multiple genes involved in chemokine pathways. Nevertheless, upon investigating the OA pathophysiology in cluster B patients as reflected by differentially expressed genes between preserved and lesioned OA cartilage (DE-OA-B genes), the chemokine genes were significantly downregulated with OA pathophysiology. Upon integrating radiographic OA data, we showed that the OA phenotype among cluster B patients, relative to cluster A, may be characterized by higher joint space narrowing (JSN) scores and low osteophyte (OP) scores.Conclusion. Based on whole-transcriptome profiling, we identified two robust OA subtypes characterized by unique OA, pathophysiological processes in cartilage as well as a clinical phenotype. We advocate that further characterization, confirmation and clinical data integration is a prerequisite to allow for development of treatments towards personalized care with concurrently more effective treatment response. Show less
Hollander, W. den; Pulyakhina, I.; Boer, C.; Bomer, N.; Breggen, R. van der; Arindrarto, W.; ... ; Meulenbelt, I. 2019
