Osteoarthritic chondrocytes undergo a glycolysis-related metabolic switch upon exposure to IL-1b or TNF

Abstract

Background: Osteoarthritis is an age-related disease that currently faces a lack of symptomatic treatment. Inflammation, which is mainly sustained by pro-inflammatory cytokines such as IL-1b, TNF, and IL-6, plays an important role in osteoarthritis progression. In this context, pro-inflammatory cytokines are widely used to mimic the inflammatory component of osteoarthritis in vitro. However, the therapeutic failures of clinical trials evaluating anti-cytokines drugs highlight the lack of overall understanding of the effects of these cytokines on chondrocytes.

Methods: Here, we generated a comprehensive transcriptomic and proteomic dataset of osteoarthritic chondrocytes treated with these cytokines to describe their pro-inflammatory signature and compare it to the transcriptome of non-osteoarthritic chondrocytes. Then, the dysregulations highlighted at the molecular level were functionally confirmed by real-time cellular metabolic assays.

Results: We identified dysregulation of metabolic-related genes in osteoarthritic chondrocytes but not in non-osteoarthritic chondrocytes. A metabolic shift, toward increased glycolysis at the expense of mitochondrial respiration, was specifically confirmed in osteoarthritic chondrocytes treated with IL-1b or TNF.

Conclusion: These data show a strong and specific association between inflammation and metabolism in osteoarthritic chondrocytes, which was not found in non-osteoarthritic chondrocytes. This indicates that the link between inflammation and metabolic dysregulation may be exacerbated during chondrocyte damage in osteoarthritis. Video Abstract.

Keywords: Chondrocytes; Inflammation; Metabolism; Osteoarthritis.

Fig. 1

Overview of the study design. OA chondrocytes (OACs) and non-OA chondrocytes (NCs) were treated for 24 h with or without pro-inflammatory cytokines. A multi-omic (RNA 3’ SRP and protein MS) approach was used in OACs to identify a pro-inflammatory signature. Then, NC transcriptomics data were compared to the OACs pro-inflammatory signature to define disease-specific expression features. Scale bar histology: 500 µm, Scale bar cells culture: 100 µm

Fig. 2

OAC and NC RNA-seq data. (a, b) Principal component analysis (PCA) was performed on the expression data of OAC (a) and NC samples (b). The first two eigenvalues were plotted with data ellipses for each treatment. (c) Summary of the number of DEGs in each condition and DEGs common between IL-1b and TNF treatments highlighted by Venn diagrams. (d, e, f, g) Volcano plots representing DEGs (p-adjust < 0.10; log2(Fold-Change) >|0.58|) in OACs (d, e) and NCs (f, g) in response to 1 ng/mL IL-1b (d, f) or 25 ng/mL TNF (e, g). (h) Histograms representing normalized MMP1, SOD2, SERPINE2, and Slc39a8 (ZIP8) gene counts in OACs and NCs; **P < 0.01 and ***P < 0.001, versus untreated condition using multiple t-tests with Benjamini & Hochberg correction; N = 5 per group. Values are expressed as means ± SEM

Fig. 3

Definition of the canonical pro-inflammatory signature in OACs. (a, b) PCAs performed on the protein expression data from (a) 5 OA patients or (b) the 4 selected patients. The first two eigenvalues were plotted with data ellipses for each patient. (c, d) Volcano plots representing DEPs (p-value < 0.05; |log2(Fold-Change)|> 0) and Venn diagram highlighting DEPs and DEGs matched in OAC in response to (c) 1 ng/mL IL-1b, or (d) 25 ng/mL TNF compared to the untreated condition. (e, f) KEGG enrichment analysis based on DEGs/DEPs in response to (e) IL-1b or (f) TNF. (g) Venn diagram highlighting 56 DEGs/DEPs common between IL-1b and TNF responses. (h) KEGG enrichment analysis using the list of 56 matched DEPs/DEGs

Fig. 4

Identification of a specific feature of OACs. (a) Venn diagram showing the 21 common DEGs among the 56 defining the pro-inflammatory signature of OACs and TNF and IL-1b responses in NCs. (b) Protein–protein network analysis using the pro-inflammatory signature identified in OACs (c) and using the 21 DEGs also found in response to IL-1b and TNF treatments in NCs. (b, c) Three interacting groups were revealed by this analysis: The blue circle group was characterized by common pro-inflammatory targets, the yellow circle group was associated with proteins involved in antigen presentation, and the green box group was composed of metabolism-related proteins. (d) RT-qPCR analysis of Glut-1, MCT-4, HK2, and GFAT2 expression in OACs and NCs after 24 h of IL-1b or TNF treatment; *P < 0.05, ** P < 0.01 using the Mann–Whitney test; N = 5 per group. Values are expressed as means ± SEM

Fig. 5

Evidence of an OAC-specific metabolic switch. (a, b) Heatmaps displaying the log₂(Fold-Change) in OACs or NCs for genes annotated in the KEGG database for (a) glycolysis/neo-glucogenesis and (b) the Krebs cycle (TCA). Heatmaps from the public transcriptome dataset (E-MTAB-6266 and GSE162510) highlighting glycolysis (c) and TCA (d) gene modulation. e Lactate measurement in OAC (N = 11) and NC (N = 11) supernatants; ns.: not significant; *P < 0.05 using the Mann–Whitney test; N = 11 per group. Values are expressed as means ± SEM

Fig. 6

Investigation of OAC-specific functional metabolic changes. a-e Seahorse® assay evaluating ECAR measurements in the glycolysis stress test in OACs (a) and NCs (b). c Quantification of glycolysis, (d) glycolytic reserve, and (e) non-glycolytic acidification from the glycolysis stress tests. f-j) Seahorse® assay evaluating OCR measurements in the mitochondrial stress assay in OACs (f) and NCs (g). (h) Quantification of basal respiration, (i) ATP production, and (j) non-mitochondrial oxygen consumption (NMOC) from the mitochondrial stress assay. k Nitrite measurements in OAC and NC supernatants; *P < 0.05, **P < 0.01 or ***P < 0.001 using the Mann–Whitney test; N = 5 (Seahorse® assays) or N = 8 (nitrite measurements) per group. Values are expressed as means ± SEM