Small RNA signatures of the anterior cruciate ligament from patients with knee joint osteoarthritis

Abstract

Introduction: The anterior cruciate ligament (ACL) is susceptible to degeneration, resulting in joint pain, reduced mobility, and osteoarthritis development. There is currently a paucity of knowledge on how anterior cruciate ligament degeneration and disease leads to osteoarthritis. Small non-coding RNAs (sncRNAs), such as microRNAs and small nucleolar RNA (snoRNA), have diverse roles, including regulation of gene expression. Methods: We profiled the sncRNAs of diseased osteoarthritic ACLs to provide novel insights into osteoarthritis development. Small RNA sequencing from the ACLs of non- or end-stage human osteoarthritic knee joints was performed. Significantly differentially expressed sncRNAs were defined, and bioinformatics analysis was undertaken. Results and Discussion: A total of 184 sncRNAs were differentially expressed: 68 small nucleolar RNAs, 26 small nuclear RNAs (snRNAs), and 90 microRNAs. We identified both novel and recognized (miR-206, -365, and -29b and -29c) osteoarthritis-related microRNAs and other sncRNAs (including SNORD72, SNORD113, and SNORD114). Significant pathway enrichment of differentially expressed miRNAs includes differentiation of the muscle, inflammation, proliferation of chondrocytes, and fibrosis. Putative mRNAs of the microRNA target genes were associated with the canonical pathways "hepatic fibrosis signaling" and "osteoarthritis." The establishing sncRNA signatures of ACL disease during osteoarthritis could serve as novel biomarkers and potential therapeutic targets in ACL degeneration and osteoarthritis development.

Keywords: anterior cruciate ligament; microRNA; osteoarthritis; small nuclear RNA; small nucleolar RNA.

FIGURE 1

Overview of HiSeq transcriptomics data between the control and diseased OA anterior cruciate ligament. (A) Categories of RNAs identified in control and diseased OA anterior cruciate ligaments (B) Principle component analysis revealed that sncRNAs between control and diseased anterior cruciate ligaments were distinctly grouped. (C) Volcano plot demonstrates significant (FDR< 0.05) differentially expressed sncRNAs (red dots) with a log2 fold change>1.3. (D) Heatmap representation of the sncRNA reads from control and OA anterior cruciate ligaments. Columns refer to the control and OA anterior cruciate ligament samples and rows of miRNAs identified with their Ensembl identification. The color of each entry is determined by the number of reads, ranging from yellow (positive values) to red (negative values).

FIGURE 2

Ingenuity Pathway Analysis-derived functions of differentially expressed miRNAs in diseased OA anterior cruciate ligaments. (A) Ingenuity Pathway Analysis identified that cellular functions such as differentiation of muscle, inflammation, proliferation, cell viability, and fibrosis were associated with the differentially expressed miRNAs. Figures generated are graphical representations of molecules identified in our data in their respective networks. Red nodes; upregulated gene expression in the OA anterior cruciate ligament and green nodes; downregulated gene expression in the OA anterior cruciate ligament. Intensity of color is related to higher fold-change. Legends to the main features in the networks are shown. The color denoting the action is dependent on whether it is predicted to be activated or inhibited. (B) Top network identified with canonical pathways overlaid for fibrosis, senescence, TGFβ signaling, RAR activation, and PPAR/RXR activation.

FIGURE 3

Osteoarthritis (OA) signaling pathway of miRNA-predicted mRNA gene targets. The canonical pathway for OA signaling was highly ranked (p = 2.33 E⁻²³) using target mRNAs identified in TargetScan from the differentially expressed miRNAs in diseased anterior cruciate ligaments derived from OA patients. The pathway was generated using Ingenuity Pathway Analysis.

FIGURE 4

Top-scoring networks derived from the 529 putative mRNAs differentially expressed in anterior cruciate ligaments derived from OA joints. Ingenuity Pathway Analysis (IPA) identified (A) “cellular development, movement and genes expression” with a score of 41. (B) “Inflammatory disease, organismal injuries and abnormalities” with a score of 35, and within this network are molecules linked to their respective canonical pathways. Both networks (A, B) are overlaid with pertinent significant biological functions contained in the gene sets. Figures generated are graphical representations of molecules identified in our data and predicted mRNA targets in their respective networks. Green nodes correspond to downregulated gene expression in anterior cruciate ligaments from OA joints, and red nodes correspond to upregulated gene expression in anterior cruciate ligament from OA joints. Intensity of color is related to a higher fold-change. Legends to the main features in the networks are shown.

FIGURE 5

Gene Ontology (GO) biological processes associated with dysregulated miRNA targets were identified following a TargetScan filter module using Ingenuity Pathway Analysis. Gene Ontology terms for biological process (FDR < 0.05) were summarized using ToppGene and visualized using REViGO and Cytoscape. Boxes represent the main clusters of biological processes that were significantly influenced by dysregulated miRNAs between control and diseased OA anterior cruciate ligaments.

FIGURE 6

Validation of small RNA sequencing miRNA results using qRT-PCR in an independent cohort. qRT-PCR results show relative gene expression normalized to miR-222, control samples n = 4, OA anterior cruciate ligament samples n = 4. The Mann–Whitney test was performed using GraphPad Prism v8.0.1, *p < 0.05.