Identified senescence endotypes in aged cartilage are reflected in the blood metabolome

Abstract

Heterogeneous accumulation of senescent cells expressing the senescence-associated secretory phenotype (SASP) affects tissue homeostasis which leads to diseases, such as osteoarthritis (OA). In this study, we set out to characterize heterogeneity of cellular senescence within aged articular cartilage and explored the presence of corresponding metabolic profiles in blood that could function as representative biomarkers. Hereto, we set out to perform cluster analyses, using a gene-set of 131 senescence genes (N = 57) in a previously established RNA sequencing dataset of aged articular cartilage and a generated metabolic dataset in overlapping blood samples. Using unsupervised hierarchical clustering and pathway analysis, we identified two robust cellular senescent endotypes. Endotype-1 was enriched for cell proliferating pathways, expressing forkhead box protein O4 (FOXO4), RB transcriptional corepressor like 2 (RBL2), and cyclin-dependent kinase inhibitor 1B (CDKN1B); the FOXO mediated cell cycle was identified as possible target for endotype-1 patients. Endotype-2 showed enriched inflammation-associated pathways, expressed by interleukin 6 (IL6), matrix metallopeptidase (MMP)1/3, and vascular endothelial growth factor (VEGF)C and SASP pathways were identified as possible targets for endotype-2 patients. Notably, plasma-based metabolic profiles in overlapping blood samples (N = 21) showed two corresponding metabolic clusters in blood. These non-invasive metabolic profiles could function as biomarkers for patient-tailored targeting of senescence in OA.

Keywords: Blood-biomarkers; Endotypes; Metabolic blood profiles; Osteoarthritis; Senescence.

Fig. 1

Study approach. 131 senescence genes were characterized in preserved cartilage (N = 57). Unsupervised hierarchical clustering and gene enrichment analysis were performed to find senescence endotypes in human aged cartilage. To find descriptive genes for the clusters, differential gene expression analysis was used. Metabolic blood profiles were extracted using unsupervised hierarchical clustering on metabolite levels in blood in an overlapping dataset with preserved cartilage (N = 21). Overlap between the cartilage endotypes and metabolic blood profiles was assessed. Unsupervised hierarchical clustering was performed on the total metabolite dataset (N = 123). Created with BioRender.com

Fig. 2

Unsupervised hierarchical clustering. A Heatmap of the two senescence endotypes. B Top 20 unique significant enriched pathways of the two senescence endotypes with their respective genes in black. C Barplot of the top 20 unique significant enriched pathways of the senescence endotypes. N = 57

Fig. 3

Characterization senescence endotypes. A STRING protein–protein network of the endotypes using endotype describing genes. B Technical validation of the genes expressed in the components. Measured by RNA-seq (left panel, N = 13) and validated by real-time qPCR (right panel, N = 8–13) showing senescence cartilage endotype-1 and -2. **P < 0.01, Student’s t-test

Fig. 4

Serum metabolite measurements. Clustering of serum metabolite expression levels (Z-score) based on Spearman’s correlation resulted in two similar clusters as on cartilage mRNA level. A In the overlapping dataset, 81% was correctly clustered (N = 21). B In all metabolite samples, 86% was correctly clustered (N = 123)