Transcriptomic changes in porcine articular cartilage one year following disruption of the anterior cruciate ligament

Abstract

To determine the transcriptomic changes seen in early- to mid-stage posttraumatic osteoarthritis (PTOA) development, 72 Yucatan minipigs underwent transection of the anterior cruciate ligament. Subjects were randomized to no further intervention, ligament reconstruction, or ligament repair, followed by articular cartilage harvesting and RNA-sequencing at three different postoperative timepoints (1, 4, and 52 weeks). Six additional subjects received no ligament transection and provided cartilage tissue to serve as controls. Differential gene expression analysis between post-transection cartilage and healthy cartilage revealed an initial increase in transcriptomic differences at 1 and 4 weeks followed by a stark reduction in transcriptomic differences at 52 weeks. This analysis also showed how different treatments genetically modulate the course of PTOA following ligament disruption. Specific genes (e.g., MMP1, POSTN, IGF1, PTGFR, HK1) were identified as being upregulated in the cartilage of injured subjects across all timepoints regardless of treatment. At the 52-week timepoint, 4 genes (e.g., A4GALT, EFS, NPTXR, ABCA3) that-as far as we know-have yet to be associated with PTOA were identified as being concordantly differentially expressed across all treatment groups when compared to controls. Functional pathway analysis of injured subject cartilage compared to control cartilage revealed overarching patterns of cellular proliferation at 1 week, angiogenesis, ECM interaction, focal adhesion, and cellular migration at 4 weeks, and calcium signaling, immune system activation, GABA signaling, and HIF-1 signaling at 52 weeks.

Fig 1. Gaussian mixture model plot.

Green and red lines represent the different gaussian distributions for “less” damaged and “more” damaged cartilage groups, respectively. The optimal separation was between macroscopic sum scores of 8 and 9.

Fig 2. Heatmaps of significantly differentially expressed genes.

Heatmaps of significantly differentially expressed genes for pooled timepoint ((A) 1-week, (B) 4-weeks, (C) 52-week) comparisons to controls. The red-blue color spectrum indicates the z-score for each gene. Surgical groups are labeled above columns, and subjects IDs are listed below columns.

Fig 3. Top 10 upregulated biological process GO terms.

The top 10 upregulated biological process GO terms based on overrepresentation analysis for (A) 1-week and (B) 4-week ACLT, RECON, and REPAIR compared to controls arranged by gene ratio (i.e., the number of genes related to the GO term divided by the total number of significant genes in the comparison). The number of genes represented by a term is indicated by the size of the circle, and the color represents the adjusted p value.

Fig 4. Treemap plots of upregulated biological process GO terms.

Treemap plots of upregulated biological process GO terms for (A) pooled 1-week vs. controls and (B) pooled 4-week vs. controls. Terms are grouped and colored according to unifying parent terms, and the amount of space a term occupies is proportional to gene set size and hypergeometric testing of overrepresented genes.

Fig 5. GSEA enrichment plots.

GSEA enrichment plots for (A) 1-week ACLT, RECON, and REPAIR compared to controls and (B) 4-week ACLT, RECON, and REPAIR compared to controls. The upper portion of each plot has lines which represent the log2 fold changes for each gene considered in the KEGG pathway. The lower portion of each plot shows the running total of these log2 fold changes, with a positive deflection representing upregulation in the first group listed in the comparison relative to the second group listed (i.e., ACLT vs. controls—first vs. second).

Fig 6. Category netplots.

Category netplots for (A) 52-week ACLT vs. controls (molecular function GO terms), (B) RECON vs. controls (downregulated cellular component GO terms), (C) REPAIR vs. controls (upregulated biological process GO terms), and (D) pooled 52-Week vs. controls (upregulated biological process GO terms) based on overrepresentation analysis. GO term size is proportional to how many genes contribute to it, and fold change of expression is color coded.