Transcriptional Analysis Reveals That the FHL1/JAK-STAT Pathway is Involved in Acute Cartilage Injury in Mice

Abstract

Objective

This study aimed to identify genes and signaling pathways associated with acute cartilage injury using RNA sequencing (RNA-seq).

Methods

Knee joint cartilage samples were collected from normal mice and 2 models of acute cartilage injury (non-invasive and groove models) within an 8-hour time limit. RNA-seq revealed differential gene expression between the injury models and controls, with subsequent validation using real-time quantitative polymerase chain reaction (RT-qPCR) for 9 representative genes.

Results

Compared to controls, the non-invasive model showed 36 differentially expressed genes (DEGs) (13 up-regulated, 23 down-regulated), with Gm14648 and Gm35438 showing the most significant upregulation and downregulation, respectively. The groove model exhibited 255 DEGs (13 up-regulated, 23 down-regulated), with Gm14648 and Gm35438 showing the (222 up-regulated, 33 down-regulated). Six overlapping genes were identified between the non-invasive and groove models, including up-regulated genes (Igfn1, Muc6, Hmox1) and down-regulated genes (Pthlh, Cyp1a1, Gm13490), validated by RT-qPCR. Gene ontology (GO) analysis highlighted involvement in environmental information processing and cartilage organ system function, while Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis implicated the JAK-STAT signaling pathway. RT-qPCR and immunohistochemistry confirmed downregulation of Fhl1 in the non-invasive model, supported by Western blotting of p-JAK2/t-JAK2 levels.

Conclusions

This study identifies DEGs (13 up-regulated, 23 down-regulated), with Gm14648 and Gm35438 showing the in acute cartilage injury, suggesting potential therapeutic targets. The role of Fhl1 in cartilage protection via the JAK-STAT pathway warrants further investigation in acute cartilage injury research.

Keywords

acute cartilage injury RNA-seq Gene

Introduction

Osteoarthritis (OA) is one of the most common chronic musculoskeletal conditions, and its prevalence has been increasing.^1,2 Post-traumatic osteoarthritis (PTOA) is a long-term complication following cartilage injury, sometimes manifesting in the mid-term. In most individuals, PTOA is a long-term complication of OA.^3,4,5 PTOA may lead to decreased physical activity and skeletal disorders and occurs mainly in young adults. At present, the exact pathogenesis of PTOA and OA is not fully understood, so there is no effective cure for neither PTOA nor OA. Research on the pathophysiology of OA/PTOA is essential for the development of effective and safe treatment and prevention strategies.^6,7,8,9

Osteoarthritis is a degenerative joint disease characterized by cartilage breakdown, resulting in pain and stiffness. Mechanical stress, genetics, and inflammation contribute to its development. Post-traumatic osteoarthritis is a specific form of OA caused by joint injury or trauma that disrupts the delicate balance of the joint and leads to cartilage degradation. Investigating the distinct pathogenic pathways of PTOA is crucial for developing targeted treatments and prevention strategies.^1,2,6
-10 A variety of animal models have been used to study the pathogenesis of OA/PTOA and the efficacy of various therapeutic methods^11-14. As Virginia Stifel ¹⁵ described, tibial plateau injuries are common and frequently lead to PTOA, particularly in military personnel, with up to 45% of patients developing PTOA after these fractures. They induced acute cartilage injury in a mouse model through tibial plateau compression loading. They assessed the injury histologically, using micro-computed tomography (µ-CT), and employing an enhanced Mankin osteoarthritis disease scoring system 5 to 11 weeks later to confirm the development of PTOA, established and characterized a convenient, reliable, non-invasive mouse model of PTOA that can be used for investigating the pathophysiology of PTOA and for developing effective therapies. In this model, articular cartilage lesions occur in relatively localized and defined areas of the tibial plateau, which is commonly observed in the clinic. However, the gene-level changes that occur in this model have not been investigated.

In recent years, transcriptomic and genomic analyses have been widely used to identify differentially expressed genes (DEGs) and study their function in different disease states.^16,17 In addition, the mechanism of several diseases has been clarified via transcriptomic analysis. Despite extensive research conducted at the cellular and molecular levels, the precise mechanism connecting acute cartilage injury to OA and the associated genes remain elusive.^18
-20 In this study, we used transcriptomics to analyze gene expression patterns in 2 mouse models of cartilage injury and identified the DEGs involved and their associated signaling pathways. Our findings could lead to the identification of new targets for the treatment and prevention of acute cartilage injury.

Materials and Methods

Ethics Statement

All experiments were conducted following approval from the Institutional Animal Welfare and Research Ethics Committee of the Third Hospital of Hebei Medical University, Shijiazhuang, China (Z 2021-018-1). We confirm that the study described in this manuscript complies with the ARRIVE guidelines as outlined in the editorial policies. Specifically, we adhered to the key ARRIVE principles, including appropriate sample size calculation, randomization, blinding, and reporting of outcome measures. We did not observe any significant deviations from the ARRIVE guidelines, as our methodologies and reporting are fully aligned with the recommended standards.

Animals

C57BL/6 mice were obtained from the Center of Experimental Animals, Hebei Medical University (Shijiazhuang, China). Although baseline differences between groups were not statistically analyzed prior to the experiment, randomization and standardized housing conditions were implemented to minimize potential confounding factors and ensure comparability across groups. All mice were housed in an animal facility maintained at 22°C to 24°C with 45% to 55% relative humidity, under a 12/12 light/dark cycle, and had ad libitum access to food and distilled water. A total of 60 (12-week-old) C57BL/6 mice were selected and randomly assigned to 3 groups: the control group, non-invasive group, and groove group. Mice in the non-invasive group, which underwent closed cartilage injury—clinically the most common form of acute cartilage injury ^6,7,10,21 were the primary focus of this study to investigate early molecular events post-injury. Comprehensive experimental analyses were conducted on both the non-invasive and control groups. The open cartilage injury models, such as the groove model used here, serve as a comparative context to identify gene expression differences arising from variations in injury type and severity. These models are particularly useful for understanding the overlap of differentially expressed genes (DEGs) between closed and open cartilage injuries. While groove models are highly valuable for evaluating early OA treatments, the degenerative changes induced by a single trauma make them more sensitive in monitoring treatment effects.

The right knee of each 12-week-old non-invasive C57BL/6J mouse (n = 20)^15,22,23 was placed in an apparatus attached to the load cell of an Instron Servohydraulic instrument under anesthetized via endotracheal delivery of halothane in a mixture of oxygen and nitrous oxide. To injure tissues at the tibial plateau, the curve of a blunt indenter blade was placed above the tibial plateau. The leg of the animal was positioned in a custom-made support, and an Instron Servohydraulic instrument was used to impact the tibial plateau under the control of a computer at a force of 55 N and a speed of 60 N/s, as shown in Fig. 1 .

Figure 1.

Schematic of the mouse modeling procedure.

Mice in the “groove” group (n = 20)²⁴ were anesthetized via endotracheal delivery of halothane in a mixture of oxygen and nitrous oxide. A 1- to 1.5-cm medial incision was made close to the ligamentum patellae in the left knee. Care was taken to prevent bleeding and soft tissue damage as much as possible. The cartilage of the lateral and medial condyles was damaged with a Kirschner wire (0.5-mm diameter) that was bent 90° 0.5 mm from the tip, restricting the depth of the groove to 0.5 mm. Two longitudinal and diagonal grooves were made on the weight-bearing parts of the femoral condyles.

The acute phase of cartilage injury features early inflammation and chondrocyte death. High inflammatory cytokines promote catabolic enzyme production and apoptosis, so early inflammation inhibition may help prevent PTOA. Within 8 hours²⁵ after the model was established, all the mice were euthanized under anesthesia, and the whole knee joints, including the synovium, adjacent tissues, and bones, were removed. The knee joints were carefully exposed, separated, immediately frozen in liquid nitrogen and stored at −80°C for RNA-seq, RT–qPCR, Western blotting, and immunohistochemistry. Three replicates were conducted.

RNA Extraction

Cartilage was collected from the knee joints of 3 mice from each group. Total RNA was extracted from the tissue using TRIzol Reagent (Plant RNA Purification Reagent for plant tissue) according the manufacturer’s instructions (Invitrogen) and genomic Deoxyribonucleic Acid(DNA) was removed using DNase I (TaKara). RNA quality was assessed post-extraction using a 2100 Bioanalyzer (Agilent) to determine the RNA Integrity Number (RIN), and RNA concentration and purity were quantified using the ND-2000 spectrophotometer (NanoDrop Technologies). Only high-quality RNA sample (OD260/280 = 1.8~2.2, OD260/230≥2.0, RIN≥6.5, 28S:18S≥1.0, >1μg) was used to construct sequencing library. These quality control checks were performed to ensure that the RNA samples were of sufficient quality for subsequent library preparation and sequencing. All 9 samples were sequenced in a single run to ensure consistency and minimize batch effects.

Library Preparation and Illumina HiSeq X Ten/Nova HiSeq 6000 Sequencing

The RNA-seq transcriptome library was prepared using the TruSeqTM RNA sample preparation Kit from Illumina (San Diego, CA) with 1μg of total RNA. Steps included polyA selection of messenger RNA using oligo(dT) beads, RNA fragmentation, and synthesis of double-stranded complementary Deoxyribonucleic Acid(cDNA) using SuperScript kit with random hexamer primers. The cDNA underwent end-repair, phosphorylation, and “A” base addition per Illumina’s protocol. Libraries were size-selected (300 bp) on 2% Low Range Ultra Agarose, PCR amplified (15 cycles with Phusion DNA polymerase), and quantified with TBS380. Paired-end RNA-seq sequencing was performed on an Illumina HiSeq X Ten/NovaSeq 6000 with 2 × 150 bp reads. RNA integrity was assessed using RIN (≥6.5), and sequencing depth was confirmed to exceed 6Gb per sample, ensuring reliable RNA-seq analysis with comprehensive gene coverage. The depth was consistent across replicates, ensuring reproducibility and high-quality data for downstream analysis. The sequencing saturation shows a uniform distribution without obvious bias, indicating uniform experimental results. In the sequencing saturation curve, when over 40% of the reads align to genes with moderate to high expression levels (quantitative values above 3.5), approaching saturation (vertical axis values approaching 1), it suggests overall high quality of saturation. This sequencing depth can cover the vast majority of expressed genes.

Read Mapping

The raw paired-end reads were trimmed and quality controlled by SeqPrep and Sickle with default parameters. Then, the clean reads were separately aligned to the reference genome in orientation mode using HISAT2 ²⁶ software. The mapped reads of each sample were assembled by String Tie via a reference-based approach.²⁷

Differential Expression Analysis and Functional Enrichment

To identify DEGs between 2 different samples, the expression level of each transcript was calculated according to the transcripts per million reads (TPM) method. RSEM was used to quantify gene abundances. Differential expression analysis was performed using DESeq2, with DEGs identified based on a|log2FC| >1 and a Q value ≤0.05. However, no specific adjustments were made to account for the small sample size during the analysis. The thresholds for significance were determined based on established practices for DESeq2. We applied the Benjamini-Hochberg (BH) method for multiple testing correction. In addition, functional enrichment analysis, including GO and KEGG analyses, was performed to identify which DEGs were significantly enriched in GO terms and metabolic pathways at a Bonferroni-corrected P value ≤0.05, compared with the whole-transcriptome background. GO functional enrichment and KEGG pathway analysis were carried out using Goatools and KOBAS. The selection of DESeq2 was justified based on its suitability for our experimental design, including considerations of biological replicates and data characteristics.

Alternative Splice Event Identification

We used the program rMATS ²⁸ to identify all alternative splicing events in our sample. We focused on isoforms that resembled the reference or had novel splice junctions. The alternative splicing events included exon inclusion, exon exclusion, alternative 5’ splicing, alternative 3’ splicing, and intron retention.

Confirmation of DEGs Via RT–qPCR

We used RT–qPCR to analyze the expression of 9 selected DEGs. The genes prioritized for RT-qPCR analysis were selected based on the following criteria: we chose the 6 overlapping DEGs between the non-invasive and groove groups to examine common patterns, the JAK-STAT pathway-related gene Fhl1 to explore pathway involvement, and 2 genes with the most significant differential expression between the control and non-invasive groups (Gm14648 and Gm35438) to investigate the most pronounced changes. The changes in the expression of 9 representative DEGs (Gm14648, Hmox1, Muc6, Igfn1, Fhl1, Gm35438, Cyp1a1, Pthlh, and Gm13490, shown in Table 1 ) were assessed. The reaction mixture consisted of 10 μL of 2×SuperReal PreMix Plus, 0.4 μL of forward primer (10 µM), 0.4 μL of reverse primer (10 µM), 2 μL of DNA template, and sterile distilled water to a total volume of 20 μL. The amplification program was as follows: initial denaturation at 95°C for 30 seconds, followed by 40 cycles of denaturation at 95°C for 15 seconds and annealing/extension at 60°C for 30 seconds. cDNA from each sample was diluted 10-fold, and 2 μL of the diluted sample was used as a template for amplification via the target gene primer and the reference gene primer. Simultaneously, melting curve analysis was performed from 60°C to 95°C. Primers with high amplification efficiency and a single-peak melting curve were selected. An iQ5 RT–qPCR instrument was used for analysis, and the relative mRNA expression was analyzed using the 2-△△CT method. The sequences of the primers for the DEGs are shown in Table 2 .

Table 1.

Specifically Selected Differentially Expressed Genes.

Gene name	FC (Non/Con)	Log2FC(Non/Con)	P value	Significant	Regulation
Gm14648	238.646	7.89872833	8.13E−09	YES	Up
Hmox1	2.158	1.10955889	5.43E−05	YES	Up
Muc6	8.572	3.09970784	2.63E−07	YES	Up
Igfn1	5.759	2.52587048	7.12E−06	YES	Up
Fhl1	0.224	−2.15754043	5.79E−05	YES	Down
Gm35438	0.136	−2.87977413	5.78E−11	YES	Down
Cyp1a1	0.0609	−4.03858248	1.48E−07	YES	Down
Pthlh	0.205	−2.28354207	6.81E−05	YES	Down
Gm13490	0.167	−2.58507458	8.16E−05	YES	Down

Table 2.

Sequences of Primers Related to the DEGs.

Gene name	Sense sequence	Antisense sequence
Gm14648	AGCAGTGGAGTGTCCTGGTA	ACTTGCCCTTGTCATGCAGT
Hmox1	AAGCCGAGAATGCTGAGTTCA	GCCGTGTAGATATGGTACAAGGA
Muc6	GCCCAACAGGAAAGTCACCAT	CTGCTTGGCCCTGATTGAGG
Igfn1	GCTGGCCTTTACCAGGTCAT	AGAGGCCCATGTCTGAGCTA
Fhl1	AGCAGTGGAGTGTCCTGGTA	ACTTGCCCTTGTCATGCAGT
Gm35438	AGGCCCGAGGGAGAAATACT	AGGCCCGAGGGAGAAATACT
Cyp1a1	GCTGTCACCGTATTCTGCCT	GCATGTGACCAATGAAGGGC
Pthlh	AGCAGTGGAGTGTCCTGGTA	ACTTGCCCTTGTCATGCAGT
Gm13490	GGGAGGCTTTCCATTTGCAC	CTCACGGGCTTTCACGTTTG

Western Blot Analysis

Total protein was extracted from mouse knee joint cartilage using lysis buffer (12,000 rpm, 4°C, centrifugation for 10 minutes). The concentration of total protein was determined using a bicinchoninic acid (BCA) protein assay kit according to the manufacturer’s instructions (Pierce, Waltham, MA). Subsequently, the proteins were separated on a 10% Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE) gel and transferred onto a Polyvinylidene Fluoride (PVDF) membrane (Millipore, Bedford, MA). After blocking with 5% skim milk in Tris-Buffered Saline with Tween 20 (TBST) buffer, the membrane was incubated overnight at room temperature with antibodies against JAK (Huabio, 1:2000; M1501-8), p-JAK (Huabio, 1:1000; ET1607-34), STAT3 (Huabio, 1:2000; ET1607-38), p-STAT3 (Huabio, 1:5000; ET1603-40), GAPDH (Huabio, 1:5000; ET1601-4), and Fhl1 (Abcam, 1:1000; AB255828). Enhanced chemiluminescence (ECL) was used to visualize all the protein bands. β-Actin was used as an internal control. Optimal antibody concentrations and incubation times were determined through systematic testing to balance staining intensity and background signal.

Immunohistochemistry

Mouse knee joints were fixed in 10% neutral formalin for 24 hours, followed by decalcification for 3 weeks in 10% ethylenediaminetetraacetic acid (EDTA). The tissue was then embedded in paraffin and sliced into 4-micron-thick sections. After deparaffinization, dehydration, and antigen retrieval, the sections were treated with 3% H₂O₂ to block endogenous peroxidase activity. Blocking was performed using 10% serum. The sections were incubated with an anti-FhlL1 antibody (Abcam, 1:1000, AB255828) overnight at 4°C. After washing with Phosphate-Buffered Saline (PBS), the sections were incubated with a biotinylated secondary antibody (Affinity, 1:10000, S0002) at room temperature for 30 minutes. Visualization was achieved using diaminobenzidine(DAB) solution. Finally, the slides were mounted and imaged using a bright-field microscope. Positive control samples were used to verify antibody specificity.

Statistical Analysis

Differences in the expression levels of 9 genes and 5 proteins between the groups were analyzed using Student’s t test with SPSS 20.0 software. All the data are presented as the means ± SDs; P <0.05 was considered to indicate statistical significance, and P < 0.01 was considered to indicate extreme significance.

Results

Analysis of Gene Alignment

After transcriptome sequencing of 9 samples, a total of 69.17 Gb of clean data were obtained. At least 6.28 Gb of clean data were obtained for each sample, with a Q30 percentage above 93.2%. The clean reads of each sample were aligned to the specified reference genome, and alignment rates ranged from 95.36 to 97.49%. A total of 31,007 expressed genes were detected in this analysis, including 29,354 known genes and 1,653 novel genes. There were 106,338 expressed transcripts, including 89,970 known transcripts and 16,368 novel transcripts ( Table 3 ).

Table 3.

Raw Data From the RNA-Seq Analysis of Non-invasive Group, “Groove” Group and Control Group.

Sample	Raw reads	Clean reads	Mapped reads	Multiple mapped reads	Uniquely mapped reads	Q20 (%)	Q30 (%)	GC content (%)
Con-1	98349958	94381628	91669678 (97.13%)	9075902 (9.62%)	82593776 (87.51%)	97.86	93.76	49.07
Con-2	45007466	43724180	42625429 (97.49%)	4782461 (10.94%)	37842968 (86.55%)	98.14	94.39	48.76
Con-3	46525162	44945010	43677283 (97.18%)	3428468 (7.63%)	40248815 (89.55%)	97.99	94.14	49.73
Non-1	45060304	43940362	42596726 (96.94%)	3855689 (8.77%)	38741037 (88.17%)	97.99	94.13	49.57
Non-2	53430490	52716268	50994604 (96.73%)	5760366 (10.93%)	45234238 (85.81%)	97.94	93.92	50.57
Non-3	44470404	42598620	40621427 (95.36%)	6151722 (14.44%)	34469705 (80.92%)	97.8	93.73	49.11
Gro-1	54382584	53330474	51760418 (97.06%)	4246255 (7.96%)	47514163 (89.09%)	98.17	94.5	50.9
Gro-2	43814998	42100654	40873689 (97.09%)	3886070 (9.23%)	36987619 (87.86%)	97.8	93.67	49.56
Gro-3	48608178	47302032	45794787 (96.81%)	3605815 (7.62%)	42188972 (89.19%)	97.64	93.2	50.71

These data indicated that the occurrence of cartilage injury may be a very complex pathological process, which involves the participation of many genes.

Analysis of Differential Gene Expression

Based on the transcriptomic data, we further identified DEGs. The results showed that 36 and 255 genes were significantly differentially expressed between the control group and non-invasive group and between the control group and groove group, respectively ( Fig. 2 ). A volcano plot illustrating the DEGs indicated that 36.11% were up-regulated and 63.89% were down-regulated in the non-invasive group, while 87.06% were up-regulated and 12.94% were down-regulated in the groove group ( Fig. 3 ). Hierarchical clustering was performed to evaluate mRNA expression profiles across cartilage samples from the control, non-invasive, and groove groups ( Fig. 4 ). Additionally, Venn analysis was employed to delineate the gene/transcript composition within each differential gene set and to identify overlapping genes between them. Compared to the control group, both the non-invasive and groove groups exhibited significantly altered gene expression profiles. Notably, 6 genes—namely Igfn1, Muc6, Hmox1 (up-regulated), Pthlh, Cyp1a1, and Gm13490 (down-regulated)—were found to overlap between the non-invasive and groove groups ( Fig. 5 ).

Figure 2.

Bar chart of the expression of significantly differentially expressed genes in the non-invasive group and groove group compared to the control group.

Figure 3.

Volcano plot of the differentially expressed genes. A: Control group vs. Non-invasive group B: Control group vs. Groove group. (red points indicate up-regulated genes, and green points indicate down-regulated genes).

Figure 4.

Heatmap of the differentially expressed genes between mice in the control group (Con-1, 2, and 3), non-invasive group (Non-1, 2, and 3), and groove group (Gro-1, 2, and 3). Red indicates highly expressed genes, and blue indicates genes with low expression.

Figure 5.

Venn diagram showing the overlapping significantly differentially expressed genes between the non-invasive group and the groove group (a total of 6 genes).

GO and KEGG Analyses

The function of the identified DEGs was investigated by GO analysis on the basis of 5 main ontologies: metabolism, environmental information processing, cellular processes, organismal systems, and human diseases, as shown in Fig. 6 . According to the metabolism, the GO term of DEGs is involved in the Metabolism of cofactors and vitamins. In the environmental information processing section, the GO term of DEGs is associated with signal transduction. In the cellular processes section, the GO term of DEGs is involved in cellular community—eukaryotes and cell growth and death. In the organismal systems section, the GO term of DEGs is involved in Immune system. In the human diseases section, the GO term of DEGs is involved in Cancer: overview.

Figure 6.

GO enrichment analysis of metabolism, environmental information processing, cellular process, organismal system, and human disease-related terms. GO, Gene Ontology.

The KEGG database was used to evaluate the pathways significantly associated with the DEGs. The DEGs were enriched in 7 KEGG pathways, such as “hepatocellular carcinoma“, “tuberculosis“, “JAK-STAT signaling pathway“, and “tight junction“, as shown in Fig. 7 .

Figure 7.

KEGG enrichment analysis of DEGs in the non-invasive group (the top 30 most enriched pathway terms). The horizontal axis denotes the enrichment factor, and the vertical axis indicates the pathway name. The histogram on the right shows the number of genes in each cluster. DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Confirmation of DEGs via RT-qPCR

To validate the gene expression data obtained by transcriptomic analysis, RT-qPCR was used to analyze the expression of 9 carefully selected DEGs, including the 6 overlapping genes in the non-invasive and groove groups, the JAK-STAT pathway-related gene Fhl1, and the 2 genes with the most significant differential expression between the control group and non-invasive group, Gm14648 (up-regulated) and Gm35438 (down-regulated), in samples from the control group and the non-invasive group. As shown in Fig. 8 , the expression levels of the 9 genes were consistent with those determined via transcriptomic analysis.

Figure 8.

The mRNA expression levels of various genes were verified through qPCR. (A) up-regulated genes and (B) down-regulated genes. The results showed consistent trends in transcription (***P < 0.001 and ****P < 0.0001, control group vs. non-invasive group).

Western Blot Analysis

There is evidence indicating that members of the JAK-STAT pathway play a crucial role in chondrocyte apoptosis and that chondrocyte apoptosis is a key factor in joint remodeling. RNA-Seq and bioinformatics analysis suggested that the JAK-STAT signaling pathway may be involved in cell apoptosis caused by acute cartilage injury. Next, we used Western blotting to confirm the p-JAK2/t-JAK2 and p-STAT3/t-STAT3 ratios and Fhl1 expression. Compared to those in the control group, Fhl1 expression was significantly decreased, and the p-JAK2/t-JAK2 and p-STAT3/t-STAT3 ratios were significantly increased in the injury group, as shown in Fig. 9 . Relative quantification of band density was performed using Western blot analysis with the aid of Image Pro Plus 6.0 software. Acute mechanical injury induced the activation of p-JAK2/t-JAK2 and p-STAT3/t-STAT3, significantly enhancing their levels by approximately 1.46-fold and 1.56-fold, respectively, compared to the control group, meanwhile decreased the expression level of Fhl1 by approximately 30% compared to the control group.

Figure 9.

Western blot analysis revealed higher p-JAK2/t-JAK2 and p-STAT3/t-STAT3 ratios in the non–invasive group (*P < 0.05 and ***P < 0.001, respectively), alongside reduced Fhl1 protein expression (*P < 0.05).

Immunohistochemical Staining of Fhl1

As mentioned earlier, the expression of the JAK-STAT pathway-related gene Fhl1 decreased in the non-invasive group. We validated this through immunohistochemistry, as shown in Fig. 10A . Compared to that in the control group, the proportion of Fhl1-positive cells in the non-invasive group decreased. ImageJ software was used to quantify the percentage of Fhl1-positive cells in both the control group and non-invasive group. For the image analysis, to prevent any potential bias, we employed a blinded approach. The images were anonymized and coded such that the analyst was unaware of the group assignments during the quantification process using ImageJ software. Statistical analysis was conducted, and the results are presented in Figure 10B . The average percentages of Fhl1-positive cells were 42.8% and 15.9% for the control and non-invasive groups, respectively. Statistical analysis using an independent samples t-test confirmed that the decrease in Fhl1-positive cells in the non-invasive group compared to the control group was statistically significant (P < 0.001).

Figure 10.

(A) Immunohistochemical staining of Fhl1-positive cells in the control group (Con) and non-invasive group (Non) (×400). (B) There was a significant statistical difference in percentages of FHL1-positive cells between the control group and non-invasive group (***P < 0.001).

Discussion

For the first time, we identified differentially expressed genes (DEGs) involved in acute cartilage injury and found that 9 genes—Gm14648, Igfn1, Muc6, Hmox1, Gm35438, Pthlh, Cyp1a1, Gm13490, and Fhl1—are associated with this condition. Notably, 6 of these genes were common to both injury groups, suggesting their potential as therapeutic targets. Among these, the role of Fhl1 in protecting cartilage via the JAK-STAT pathway warrants further investigation. Understanding how Fhl1 influences cartilage preservation and its interaction with the JAK-STAT signaling pathway could provide valuable insights into the mechanisms of acute cartilage injury and its progression to PTOA. This line of research may help identify novel therapeutic strategies for mitigating cartilage damage and improving outcomes in PTOA.

The incidence of PTOA associated with acute cartilage injury is gradually increasing. In recent years, with increases in physical activity and body weight, health problems in younger people have become more severe.^7,8,10,25,29 The prevention and early diagnosis of PTOA and the improvement of patient prognosis and quality of life are the current directions of relevant biomedical research. Although extensive research has been conducted on OA, including PTOA, in recent years, there has been no consensus regarding the early molecular mechanisms of PTOA caused by acute cartilage injury.

Recently, progress in transcriptomics techniques has opened up avenues to pinpoint genes associated with early acute cartilage injury. We conducted in-depth research on the related molecular mechanism, which is highly useful for elucidating the early pathogenesis of PTOA caused by acute cartilage injury. A total of 36 genes were identified in the non-invasive group compared to the control group, 13 of which were up-regulated and 23 of which were down-regulated. The most significantly up-regulated gene was Gm14648, and the most significantly down-regulated gene was Gm35438. A total of 255 genes were identified in the groove group compared to the control group, with 222 up-regulated and 33 down-regulated. Among the significantly DEGs, there were 6 overlapping DEGs between the non-invasive group and the groove group, including 3 up-regulated genes (Igfn1, Muc6, and Hmox1) and 3 down-regulated genes (Pthlh, Cyp1a1, and Gm13490). We validated the results through RT-qPCR, the results of which were consistent with the sequencing results. The significant changes in the expression of these genes indicate their potential involvement in the early inflammatory response after acute cartilage injury, but the exact underlying mechanisms still need further investigation. RNA-seq and RT-qPCR offer insights into gene expression, but adding proteomics could deepen our understanding of early molecular responses in cartilage injury by revealing protein changes and interactions that transcriptomics might miss. The greater number of DEGs in the groove group compared to the non-invasive group likely reflects the increased severity and broader inflammatory response associated with the more invasive injury model. This variation highlights the impact of injury severity on gene expression profiles. Gm14648 encodes the 60S ribosomal protein L21, which is essential for ribosome assembly and protein translation, highlighting its crucial role in cellular protein synthesis. Muc6 is occasionally expressed in cartilage, and this expression may be associated with metabolic changes in the tissue, but further research is needed to determine its specific role and significance.³⁰ GM35438 and IGFN1 genes play crucial roles in skeletal muscle cell function. IGFN1 functions by influencing cellular aging and maintenance of function, while GM35438 is associated with the regulation of skeletal muscle contraction function.^31,32 HMOX1 may inhibit osteoclast activation in subchondral bone, contributing to the slowing of OA progression.³³The expression of Cyp1a1 is positively correlated with the severity of OA, suggesting that Cyp1a1 may play a role in the pathogenesis of OA.³⁴ While these DEGs have been identified and their general functions reviewed, their exact contribution to acute cartilage injury and whether they have protective or detrimental effects remains uncertain. In addition to these genes, approximately 250 DEGs were unknown, which were not annotated during this study, and these genes will be the focus of future studies.

Gene ontology enrichment analysis can be used to identify the functions of genes and gene products in an organism. Kyoto Encyclopedia of Genes and Genomes enrichment analysis can provide information regarding the functions of genes involved in metabolic pathways.^17,35 In our study, through GO analysis, we found that acute mechanical injury mainly affects environmental information processing and organismal systems in cartilage. Further analysis revealed that it primarily affects signal transduction in cells. Kyoto Encyclopedia of Genes and Genomes analysis revealed that the JAK-STAT signaling pathway is one of the important pathways involved in this process. The JAK/STAT signaling pathway is involved in many important physiological activities, such as cell proliferation, differentiation, immune regulation, and apoptosis.^36,37 Fhl1 (down-regulated expression), a gene related to this pathway, attracted our attention, and we validated the change in the expression of this gene through RT–qPCR, the results of which were consistent with the sequencing results. Immunohistochemistry further confirmed that Fhl1 was expressed at low levels in cartilage in the non-invasive group.

Members of the Fhl (4 and a half LIM) family have been found to be expressed in various organs, including muscle, heart, kidney, and lungs, with Fhl1 being predominantly expressed in skeletal and cardiac muscle. Fhl1 mutations can lead to diverse skeletal and cardiac muscle diseases.^38
-40 Recent studies have indicated that low Fhl1 expression may serve as a prognostic marker for poor outcomes after lung adenocarcinoma surgery.⁴¹ A few studies have also suggested that Fhl1 may play a role in bone- and joint-related cartilage injuries. Friese et al.⁴² reported that FHL-1, a crucial factor in complement regulation, plays a protective role against inflammation in rheumatoid arthritis (RA), particularly when it is highly expressed in synovial lining cells, which has significant physiological implications. However, their study was confined to RA and did not address the associated cell channel. Joos et al.⁴³ reported that IL-1β significantly downregulates the expression of Fhl1, Fhl2, and other genes related to cytoskeletal components in OA chondrocytes, which contributes to the development of degenerative joint diseases. This study partially demonstrated the protective effect of Fhl1 on articular cartilage, which is consistent with our findings. In the acute cartilage injury model group, we observed Fhl1 downregulation, suggesting that Fhl1 is indeed a protective gene associated with cartilage. The differential regulation of Fhl1—downregulated in the non-invasive model and stable in the groove model—suggests its protective role in mild injuries and indicates potential compensatory mechanisms in more severe cases.

The role of the JAK-STAT pathway in the pathogenesis of OA has been extensively studied. The JAK-STAT signaling pathway interacts with MAPK and other signaling pathways in cartilage, collectively participating in the pathophysiological processes of OA.⁴⁴ In vitro studies on mechanically stimulated articular cartilage and chondrocytes have indicated that IL-4 mediates OA through the JAK-STAT pathway.⁴⁵ IL-7 activates the JAK/STAT pathway in human articular chondrocytes, leading to increased phosphorylation of JAK-3 and STAT-3, which results in increased production of inflammatory factors.⁴⁶ Both IL-9 and IL-21 promote cartilage degradation and lead to femoral head necrosis by activating the JAK-STAT signaling pathway and increasing the phosphorylation of STAT-1 and STAT-3.^47,48 Anti-inflammatory cytokines like IL-10, and IL-13 promote alternative macrophage polarization via the JAK/STAT pathway.⁴⁹ RA has been extensively studied, and it has been suggested that activation of JAK-STAT leads to joint destruction induced by arthritis.^50,51 The JAK/STAT signaling pathway is closely associated with RA. STAT1 and STAT3 are 2 primary transcription factors in the JAK/STAT signaling pathway, playing crucial roles in the pathogenesis of RA.⁵²Current research has found that inhibiting the JAK/STAT signaling pathway can reduce the expression of pro-inflammatory cytokines, showing potential in the treatment of arthritis as a promising target.^53
-56 Our findings show a significant increase in the p-JAK2/t-JAK2 and p-STAT3/t-STAT3 ratios in the injury group, aligning with previous studies. For example, Rong et al.⁵⁷ linked elevated JAK2 levels in OA chondrocytes to reduced cartilage balance and increased apoptosis. Similarly, Shao et al.^58,59 found higher JAK2 and STAT3 levels in OA cartilage, associated with matrix damage and lower COL-II levels. Teng et al.⁶⁰ also noted that IL-1β’s impact on inflammatory cytokines in OA chondrocytes was mediated through the JAK2/STAT3 pathway. This further supports our observation of increased p-JAK2/t-JAK2 and p-STAT3/t-STAT3 ratios, indicating significant activation of this pathway in our injury model.

In summary, while both Fhl1 and the JAK-STAT pathway have been implicated in the occurrence and development of OA, no study has yet specifically identified a relationship between Fhl1 and the JAK-STAT pathway. However, through RNA-seq analysis, we found that Fhl1 (down-regulated) was significantly differentially expressed in the non-invasive group and enriched in the JAK-STAT pathway. Additionally, Western blot analysis confirmed that, compared to the control group, the p-JAK2/t-JAK2 and p-STAT3/t-STAT3 ratios were increased, and the relative protein expression level of Fhl1 was decreased in the non-invasive group. These findings suggest that Fhl1 may protect knee articular cartilage through the JAK-STAT pathway. However, further research is needed to confirm the association between Fhl1 and the JAK-STAT pathway. To further validate this hypothesis, future research could incorporate gene knockdown or overexpression studies in cartilage cells, as well as in vivo models like Fhl1 knockout mice. Additionally, using specific inhibitors and validating across cell types will help better understand the interaction between Fhl1 and the JAK-STAT pathway. This could involve using other experimental techniques such as immunoprecipitation, co-immunoprecipitation analysis, or functional assays to thoroughly explore the interaction mechanisms between JAK-STATs and Fhl1, and their roles in cellular signaling pathways. Future research should also consider the interactions between Fhl1 and other key genes to explore their synergistic effects, thereby developing a more comprehensive therapeutic strategy to enhance the efficacy of cartilage repair. These findings emphasize the importance of Fhl1 and the need for further research. Our study suggests that the downregulation of Fhl1 may serve as a pharmacological target for enhancing cartilage protection, with future exploration of strategies such as growth factors, small molecule inhibitors, gene therapy, and natural compounds.

Our studies have highlighted the potential chondroprotective effect of Fhl1 through the modulation of the JAK-STAT pathway, suggesting its potential for the treatment of acute cartilage injury, which may be closely related to PTOA. Future research could explore drug therapies targeting Fhl1/JAK-STAT, potentially utilizing novel small molecule drugs or biologics, to regulate these pathways and decelerate or prevent acute cartilage injury. However, targeting the JAK-STAT pathway holds therapeutic promise but requires careful consideration of potential risks, including impacts on normal tissue, immune balance, and drug specificity. Moreover, these findings hold promise as biomarkers for the early diagnosis and monitoring of acute cartilage injury, with the potential to enhance patient prognosis and quality of life through innovative diagnostic and therapeutic strategies derived from genetic and signaling pathway insights. Nevertheless, applying new genes to cartilage damage treatment involves challenging steps such as validating gene functions, confirming biomarkers, and evaluating drug targets, which require interdisciplinary collaboration. There are certain limitations in this study. While mouse models may not fully replicate human physiology and require further validation, studying larger animal knees could yield more reliable results and provide stronger scientific evidence for clinical treatment. Our study did not include a sham surgery group, which may affect the interpretation of the specific effects of the intervention. The absence of a sham group makes it difficult to distinguish gene expression changes caused by cartilage injury from those induced by the surgical procedure. In future studies, we will include a sham group that undergoes all surgical procedures except cartilage injury, with samples collected at the same time points for more accurate analysis. The limited sample size may impact the reliability of the results; increasing the sample size would enhance credibility. Restricting sample collection to within 8 hours after injury limits our understanding of temporal changes in gene expression. Future research should incorporate longitudinal studies, collecting and analyzing samples at various time points post-injury (such as immediately, 24 hours, 1 week, 1 month, etc.) to gain a more comprehensive understanding of gene expression changes. No testing of potential therapies was conducted, necessitating future research to evaluate treatment effectiveness for different patterns of gene expression.

In conclusion, our research highlights that acute cartilage injury is associated with the dysregulation of several DEGs. Detailed examination of these 9 DEGs could reveal new therapeutic targets for managing acute cartilage injury. Furthermore, these identified DEGs have the potential to serve as biomarkers for the early diagnosis and monitoring of acute cartilage injury, offering insights that could improve the understanding and treatment of conditions such as PTOA. Translating these DEGs into clinical biomarkers is challenging, but combining them with existing biomarkers like Matrix Metalloproteinase-3 (MMP-3) and Chondroitin Sulfate 6S(C-6S) could enhance diagnostic accuracy.⁶¹ Additionally, exploring the potential cartilage-protective effect of Fhl1 through the modulation of the JAK-STAT pathway could open up new avenues for future research. Exploring targeted therapies via the Fhl1/JAK-STAT pathway could involve testing small molecule inhibitors or biologics in preclinical models. Further mechanistic studies to elucidate the roles of identified DEGs and pathways in cartilage injury and repair would guide therapeutic development.

Footnotes

Acknowledgment and Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Central Guide Local Science and Technology Development Foundation of Hebei Province (grant no. 216Z7708G)

Author Contributions

All the authors contributed to the study conception and design. J.L. conceptualized and designed the study, with contributions from Z.S. and L.G. who conducted experiments and collected data. D.R. and H.H. performed data analysis and interpretation. G.R. and S.Y. contributed to manuscript preparation and revisions. P.W. supervised the project. The first draft of the manuscript was written by J.L., and all the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics Approval and Consent to Participate

All experiments were performed after receiving approval from the Institutional Animal Welfare and Research Ethics Committee of the Third Hospital of Hebei Medical University, Shijiazhuang, China.

ORCID iD

Pengcheng Wang

Availability of Data and Material

All the data and materials that are required to reproduce these findings can be obtained by contacting the corresponding author, wangpc999@hebmu.edu.cn.

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