Comprehensive microRNA profiling of aging cartilage in the Dunkin-Hartley guinea pig model identifies key regulators of osteoarthritis progression

Comprehensive microRNA profiling of aging cartilage in the Dunkin-Hartley guinea pig model identifies key regulators of osteoarthritis progression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive microRNA profiling of aging cartilage in the Dunkin-Hartley guinea pig model identifies key regulators of osteoarthritis progression Nidhi Bhardwaj, Diksha Rana, Alka Bhatia, Uttam chand Saini, Jyotdeep Kaur This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8914957/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Osteoarthritis (OA) is the leading cause of joint disability in the elderly, particularly affecting the knee. The molecular mechanisms underlying idiopathic, age-related OA remain unclear, and current therapies are largely symptom-targeted. MicroRNAs (miRNAs) are key regulators of cartilage homeostasis and inflammation, but most in vivo models fail to recapitulate idiopathic, age-associated OA. Methods The Dunkin-Hartley guinea pig model of spontaneous OA was used to investigate aging-related miRNA dysregulation. Cartilage RNA was extracted from control, early OA, and late OA groups and profiled for 100 miRNAs using RT-qPCR arrays. Histological evaluation and OARSI scoring assessed cartilage degeneration. Dysregulated miRNAs were analyzed using a bioinformatic pipeline incorporating target prediction (TargetScan, miRTarBase, DIANA microT-CDS), protein-protein interaction (STRING), network analysis (Cytoscape), and pathway enrichment (KEGG). Functional validation was performed by transfecting SW1353 cells with miR-7-5p mimics and assessing expression of predicted target genes. Results Histological analysis and OARSI scoring confirmed progressive cartilage degeneration. RT-qPCR arrays revealed significant age-dependent downregulation of miR-130b-3p, miR-7-5p, miR-20b-5p, and miR-130b-5p, with 38 miRNAs downregulated in late OA relative to controls. Network and pathway analysis identified miR-7-5p as a central regulator of OA pathogenesis. Transfection of SW1353 cells with miR-7-5p mimics suppressed expression of predicted gene targets, including RELA(p65), RAF-1, TGFB2, IRS1, SNCA, PIK3CD, EIF4E , and ARRB1 , confirming an inverse regulatory relationship. Conclusion This study delineates aging-related dysregulated miRNAs in idiopathic OA and highlights miR-7-5p as a key regulator of disease pathogenesis. The study highlights the Dunkin-Hartley guinea pig as a valuable translational model for exploring primary OA pathogenesis and advancing miRNA-based therapies in OA. cartilage Dunkin-Hartley guinea pig miRNA osteoarthritis idiopathic OA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Key Message What is already known on this topic: Osteoarthritis lacks effective disease-modifying therapies, and aging-related idiopathic OA remains poorly captured by existing animal models. What this study adds: The Dunkin-Hartley guinea pig model of idiopathic OA reveals age-dependent downregulation of cartilage miRNAs. How this study might affect research, practice or policy: miR-7-5p acts as a central regulator of OA pathogenesis and emerges as a potential therapeutic target, supporting the development of miRNA-based disease-modifying therapies beyond symptom management. 1. Introduction Osteoarthritis (OA) is the prevailing reason for joint disability, particularly in the knee joint ( 1 ). Certain ethnic and racial groups, particularly older individuals, women, and individuals with lower socioeconomic status, bear a heavier burden of the condition ( 2 ). Although OA is most closely correlated with aging, joint injury, obesity, occupation-related factors and mechanical stresses are also responsible for the progression of OA ( 3 ). Despite its widespread impact, OA remains a complex, multifactorial disease with poorly understood molecular mechanisms. Current treatments primarily focus on symptom relief, and there is a critical unmet need for disease-modifying therapies that can halt or reverse OA progression. MicroRNAs (miRNAs) function as key players in post-transcriptional gene regulation, influencing multiple biological pathways, including cartilage homeostasis and inflammation ( 4 , 5 ). These small non-coding RNAs modulate the stability and translation of target mRNAs, thereby influencing chondrocyte function, extracellular matrix (ECM) turnover, and inflammatory responses. Dysregulation of specific miRNAs, such as miR-140 and miR-146a, has been consistently reported in OA ( 6 – 11 ), illustrating their potential to serve not only as biomarkers for clinical diagnosis but also as intervention targets. Several miRNA-based therapies are currently in clinical trials for various diseases ( 12 , 13 ). However, none have yet been developed specifically for OA. Given their central regulatory roles, miRNAs offer promising avenues for developing novel epigenetic-based therapies aimed at restoring joint homeostasis. However, despite growing interest, significant gaps remain in understanding the dynamic miRNA changes during the natural course of OA, especially in age-related idiopathic cases where disease triggers are not well defined. Most human studies rely on late OA tissue samples, which may not accurately capture early molecular events driving OA pathogenesis. Additionally, many animal models focus on surgically induced OA, which does not fully replicate the age-related spontaneous progression observed in humans. These limitations underscore the need for profiling miRNA expression in models that better mimic the natural onset and progression of idiopathic OA. The Dunkin-Hartley guinea pig model is widely recognised for its close resemblance to human primary OA, developing spontaneous cartilage degeneration and histopathological features with age ( 14 , 15 ). This animal model is, therefore, translationally relevant for studying early disease mechanisms and evaluating therapeutic strategies. In this study, we employed the Dunkin-Hartley guinea pig model and, for the first time, studied the PCR array-based profiling to identify miRNAs dysregulated during spontaneous OA progression. The advancement in RT-qPCR array technology enabled the sensitive profiling of miRNAs from limited cartilage tissue, facilitating the discovery of key regulatory miRNAs involved in OA progression. Our approach aimed to capture dysregulated miRNAs during the disease progression that may contribute to OA pathogenesis. By employing a comprehensive OA-focused in silico approach, we identified key miRNAs with potential regulatory roles in OA. We also conducted in vitro experiments to validate the regulatory influence of candidate miRNA on their target genes. Focusing specifically on age-associated idiopathic OA and utilising a relevant animal model, this study offers an insightful overview of miRNAs' involvement in the progression of the disease and lays the foundation for future miRNA-based therapeutic strategies. 2. Methods 2.1 Experimental animals and articular cartilage collection Male Dunkin-Hartley guinea pigs ( Cavia porcellus ) were obtained after ethical clearance from the Institute Animal Ethics Committee (IAEC) of Postgraduate Institute of Medical Education and Research, India. All animals were housed in standard stainless-steel guinea pig drawer-style trolleys (36'' x 23'' x 9'') in pairs and had free access to certified guinea pig diet (Hindustan Animal Feeds, Guinea Pig Grower Feed) and tap water. Thirteen guinea pigs were allocated into three groups for grade-wise analysis on the basis of their age and weight. The 3-month-old guinea pigs (283 ± 25 g) served as the control group (n = 4), while the 7-month-old guinea pigs (600 ± 10 g) represented the early OA group (n = 4) and the 12-month-old guinea pigs (835 ± 15 g ) represented the late OA group (n = 5). The animals were euthanized with lethal doses of ketamine: xylazine (85 mg/kg: 15 mg/kg), and the knee joints were harvested. For histological examination, one knee joint was fixed in 10% neutral buffered formalin (NBF), while the other joint was dissected on ice and the connective tissue and muscle was removed. Articular cartilage was then scraped from the femoral and tibial condyles using a surgical blade. 2.2 RNA isolation and miRNA profiling The cartilage samples flash-frozen using liquid nitrogen were homogenized using a mini-beadbeater-16 (Biospec, Cat. no. 607), and total RNA was isolated using the Quick-RNA™ Miniprep Plus Kit (Zymo Research, Cat. no. R1057) with some modifications as previously demonstrated ( 16 ). The RNA concentration, RNA quality, A 260:280 , and A 260:230 were determined spectrophotometrically (Supplementary Data 1, Table S1 ) using the NanoDrop®-ND 1000 spectrophotometer. RNA was then reverse transcribed using miRCURY LNA RT Kit (Qiagen, Cat. no. 339340) for miRNA cDNA synthesis. RT-qPCR arrays provide the means to profile hundreds of miRNAs in a single experiment. Pre-dispensed miRNA-specific primer sets were customized in 384-well plates for SYBR Green-based detection using miRCURY LNA Custom PCR Panel (Qiagen, Cat. no. 339330). Expression of 100 miRNAs was analyzed in cartilage samples from the control, early OA (n = 4), and late OA group (n = 5). A total of 2 ng of RNA was utilized in each well of the PCR array plate for a 10 µL SYBR Green-based RT-qPCR reaction performed in duplicates (Qiagen, Cat. no. 339345). The transcripts were quantified using the following program: initial hot start for 2 min at 95°C, followed by 40 cycles of 95°C for 10 s, 56°C for 1 min (CFX384 Touch Real-Time PCR Detection System BIORAD). The plates were inspected by melting curve analysis after amplification (Supplementary Data 1 Fig. S1 - S3 ) to confirm a single PCR product in each well. The raw Ct values were used for relative quantification using the GeneGlobe Data Analysis software (Qiagen), and the threshold cycle (Ct) cut-off was set to 38. The Ct values of target miRNAs were normalized using the global Ct mean of expressed miRNAs method, and the differentially expressed miRNAs with p-value ≤ 0.05 and fold change ≥ 2.0 were selected for further analysis. The miRNA dysregulation was expressed as fold regulation, representing the relative change in expression compared to the control group. Negative values indicated downregulation, whereas positive values indicated upregulation. 2.3 miRNA selection strategy for RT-qPCR arrays A subset of 100 miRNAs was chosen for miRNA profiling using RT-qPCR arrays (Supplementary Data 1, Table S2 ). A combination of database mining, literature review, and laboratory data was used to identify miRNAs potentially involved in OA (Fig. 1 ). The Homo sapiens mature miRNAs were retrieved from the miRBase database, totalling 2,654 miRNAs. Similarly, Cavia porcellus (guinea pig) miRNAs were also fetched from miRBase, comprising 656 miRNAs. This was followed by mapping the miRNomes of the two species, which revealed 503 common miRNAs shared between the species. These miRNAs were further screened on the basis of three filters: (i) miRNAs reported in OA, (ii) bone-expressed miRNAs, and (iii) miRNAs differentially expressed in synovial fluid samples from OA patients, based on previous microarray data from our lab. Literature mining revealed the potential involvement of 52 miRNAs associated with OA. The identification of miRNAs exhibiting high expression in bone was done using the human miRNA tissue atlas, and a total of 41 miRNAs were selected based on their tissue specificity index (TSI) of ≥ 0.75. The TSI ranges from 0 to 1, with lower values (close to 0) reflecting ubiquitous expression of miRNAs and higher values (close to 1) indicating tissue-specific expression. The comparison with the previous miRNA microarray data of our lab in synovial fluid samples from OA patients revealed 7 common miRNAs, which were also included for miRNA profiling in the cartilage. 2.4 Histology and OARSI scoring The knee joints were routinely fixed with 10% NBF for 20 days and then decalcified with 5% nitric acid for 4 to 6 weeks at room temperature (22 ± 5°C), changing the solution weekly. The knee joints were sectioned coronally and placed in cassettes, followed by rinsing the specimens with water before being embedded in paraffin. Each sample was then coronally sectioned using a microtome (Slee, Cut 5062) at a thickness of 6 µm. The sections were subsequently dewaxed in xylene and hydrated using a graded series of ethanol before undergoing staining with (H&E) haematoxylin and eosin (Sigma, Cat. No. H9627) for the general assessment of tissue morphology. We also performed Safranin O-Fast green (Sigma, Cat No. S2255) and toluidine blue (Sigma, Cat No. 89640) staining for the assessment of proteoglycan content in the cartilage. Articular cartilage changes were evaluated by a single-blinded observer and scored using the Osteoarthritis Research Society International (OARSI) semi-quantitative system validated for use in guinea pigs. ( 17 ). The OARSI scoring method considers five aspects of cartilage health: structural alterations, proteoglycan content, cellularity, tidemark condition, and osteophyte formation. The pictures of stained slides were taken using the EVOS™ FL Auto Imaging System. 2.5 In-silico analysis for miRNA target prediction The top dysregulated miRNAs were selected for in-silico target gene prediction (Fig. 2 ) to delve into their plausible role in regulating the pathogenesis of OA. Three miRNA target prediction software programs (TargetScanHuman 7.1, miRTarBase, and DIANA-microT-CDS) were used to predict the gene targets of each of the 12 top dysregulated miRNAs. TargetScanHuman 7.1 ( https://www.targetscan.org/vert_80/ ) predicted the de novo target genes, miRTarBase https://tools4mirs.org/software/mirna_databases/mirtarbase/ predicted the experimentally verified target genes, and DIANA microT-CDS http://www.microrna.gr/microT-CDS predicted the target genes that, in addition to binding to the 3'-UTR of the mRNA, also bind via their coding sequence to the miRNA. The common gene targets from the prediction tools were identified, providing a focused set of genes likely to be regulated by the selected miRNAs. The common target genes served as input for building a protein-protein interaction (PPI) network via STRING https://string-db.org/ for comprehensive interaction analysis. The hub genes in the network were analysed using the cytoHubba plug-in of Cytoscape software (version 3.9.1). CytoHubba provided an intuitive interface for the exploration and identification of critical nodes within the network that are closely associated with OA. The common gene targets of each miRNA were further analyzed for pathway enrichment using the KEGG database via ShinyGO https://bioinformatics.sdstate.edu/go77/ , a web-based application designed to extract actionable insights from gene sets and elucidate the associated molecular pathways. In addition, the OsteoDIP database https://ophid.utoronto.ca/OsteoDIP/Home.faces was also employed to specifically filter and identify genes that are associated with OA from the list of common targets ( 18 ). To further shortlist the top target genes for each miRNA, a comparison was made between the hub genes, the genes involved in at least two OA-related pathways, and the OA-related genes identified through the OsteoDIP analysis. The genes that were common in at least two of these lists were selected as the candidate target genes for each miRNA. This strategy integrated multiple bioinformatics tools, allowing a focused identification of miRNA target genes and ensuring their relevance to OA. 2.6 Cell Culture Chondrosarcoma cell line SW1353 was obtained from ATCC (American Type Culture Collection), USA. Cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM, Himedia) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Himedia, Cat. No. A002A). Cells were maintained at 37°C in a humidified incubator with 5% CO₂. The medium was replaced every 2–3 days, and cells were passaged upon reaching 80–90% confluency using trypsin-EDTA (Himedia, Cat. No. TCL070). For all experiments, cells between passages 27–30 were used to ensure consistency and reproducibility. 2.7 Transfection and RT-qPCR SW1353 cells were seeded in 24-well plates at a density of 9 × 10⁴ cells per well. After 24h, when cells reached 60–70% confluency, the medium was replaced with antibiotic-free DMEM supplemented with 5% FBS. Cells were transfected with 50 nM miR-7-5p mimics or 100 nM miR-7-5p inhibitors (miRCURY LNA miRNA Mimic/Inhibitor, QIAGEN) using HiPerfect Transfection Reagent (Qiagen) following the manufacturer’s protocol. The HiPerfect–oligonucleotide complexes were prepared in serum and antibiotic-free DMEM. Scrambled negative control oligonucleotides (Qiagen) were transfected at equivalent concentrations (50 nM and 100 nM) to account for non-sequence-specific effects. 48h post-transfection, total RNA was extracted using TRIzol® reagent (Invitrogen), and RNA concentration and purity were assessed using a NanoDrop® ND-1000 spectrophotometer (Thermo Fisher Scientific). For miRNA quantification, 200 ng of total RNA was reverse-transcribed using the miRCURY LNA RT Kit (QIAGEN), and RT-qPCR was performed using the miRCURY LNA SYBR Green PCR Kit with specific primers for hsa-miR-7-5p (Cat. No. 339306, Qiagen). U6 snRNA served as the endogenous control for normalization. For mRNA expression analysis of target genes, including RELA(p65), RAF-1, TGFB2, SNCA, PIK3CD, IRS-1, EIF4E , and ARRB1 , Reverse transcription of 1 µg total RNA was carried out using the iScript™ cDNA Synthesis Kit (Bio-Rad). RT-qPCR was carried out using gene-specific primers (listed in Supplementary Data 4, Table S1 ), with GAPDH as the internal control. 2.8 Statistical Analysis All quantitative analyses of both in vivo and in vitro data were performed using a minimum of three biological replicates, with technical replicates included where applicable. The maximum number of animals permissible under our animal facility’s logistical guidelines was used. OARSI scores were analyzed using one-way ANOVA followed by Tukey’s post hoc test. For in vitro experiments and RT-qPCR array data, statistical significance was determined using an unpaired t-test. Statistical analyses were conducted using GraphPad Prism version 9, and a p-value < 0.05 was considered statistically significant. Target genes of selected dysregulated miRNAs were predicted using TargetScanHuman, miRTarBase, and DIANA-microT-CDS. The common gene targets were analyzed to ensure biological relevance. Identified gene targets were analyzed via STRING for PPI network construction and hub gene identification using Cytoscape. Pathway enrichment analysis was performed using KEGG via ShinyGO, and OA-related genes were filtered using the OsteoDIP database. Candidate genes for each miRNA were shortlisted by selecting those that appeared in at least two of the following categories: hub genes, OA pathway-associated genes, and OA-related genes from OsteoDIP. 3. Results 3.1 Osteoarthritic changes progress with advancing age in Dunkin-Hartley guinea pigs The animals from the control (n = 4), early OA (n = 4), and late OA (n = 5) groups were euthanised at 3, 7 and 12 months respectively. Following dissection, the femoral and tibial articular cartilage surfaces were assessed for age-associated degenerative changes. Macroscopic evaluation revealed a clear progression in cartilage damage across the groups. In the control animals, the articular surfaces appeared smooth, glossy, and continuous, reflecting normal cartilage morphology with no visible signs of degeneration. In contrast, the early OA group exhibited mild surface irregularities and the presence of superficial fibrillations indicative of the initial stages of cartilage deterioration. The late OA animals demonstrated pronounced structural damage, characterised by deep fibrillations, extensive surface roughening, and exposure of the underlying subchondral bone (Fig. 3 A). The histopathological evaluation of tibial and femoral cartilage from the control, early OA, and late OA groups was performed using the OARSI scoring system to assess the severity of osteoarthritic changes in the Dunkin-Hartley guinea pigs. The OARSI system quantifies cartilage damage based on the depth and extent of degeneration, with higher scores indicating more severe pathology. Each individual section was assigned a maximum possible score of 18, reflecting the extent of articular cartilage degeneration. The results revealed an age-dependent progression of osteoarthritis, as evidenced by histopathology (Fig. 3 B) and increased OARSI scores. In the control group, H&E-stained sections displayed a smooth and continuous articular surface, normal chondrocyte distribution, and a single, well-defined tidemark which delineates the transition between calcified and uncalcified cartilage. The early OA group exhibited mild surface irregularities and slight hypercellularity, although the tidemark remained intact. In contrast, the late OA group demonstrated pronounced degenerative features, including deep surface fissures extending into the middle zone, marked hypercellularity with prominent chondrocyte clustering. Tidemark duplication, indicative of its progression into noncalcified cartilage, was a prominent feature The proteoglycan content assessment (Fig. 4 A) revealed that in the control group, both safranin O-fast green and toluidine blue demonstrated intense and uniform staining throughout the articular cartilage, indicating abundant proteoglycan content. The early OA group showed a noticeable reduction in staining intensity, particularly in the superficial zone, reflecting depletion of glycosaminoglycans (GAGs). In the late OA group, staining intensity was markedly diminished across the superficial, middle, and deep zones, consistent with advanced proteoglycan loss and severe cartilage degeneration. The overall OARSI scores (Fig. 4 B) in the control group ranged from 0 to 1, consistent with normal cartilage morphology and the absence of degenerative changes. In the early OA group, scores ranged from 3 to 5, indicative of mild but discernible structural deterioration. The late OA group exhibited OARSI scores ranging from 8 to 12, reflecting advanced cartilage degeneration (Fig. 4 B). Statistical analysis using one-way ANOVA followed by Tukey’s post hoc test revealed significant differences between the control and early OA groups (p < 0.001), the control and late OA groups (p < 0.0001) and also the early and late OA groups (p < 0.001). Collectively, these findings validate the progressive, age-associated nature of osteoarthritic changes in the Dunkin-Hartley guinea pig model. 3.2 RT-qPCR arrays revealed a downregulation of miRNAs in cartilage correlating with OA progression The RT-qPCR array analysis of 100 miRNAs (listed in Supplementary Data 1, Table S2 ) potentially associated with OA revealed a predominance of downregulation across all comparisons. Three distinct analyses were performed: early OA vs. control, late OA vs. control, and late OA vs. early OA. This comparative framework allowed the identification of miRNAs dysregulated at disease onset as well as those altered during disease progression. The RT-qPCR array profiling of the selected panel of 100 miRNAs revealed a predominance of downregulated miRNAs across all three comparisons. This trend suggests a global suppression of miRNA expression in OA cartilage, potentially reflecting widespread disruption of regulatory networks involved in cartilage homeostasis and OA pathophysiology. In the early OA vs. control group, two miRNAs, miR-101-3p (3.02-fold regulation) and miR-92b-3p (3.25-fold regulation), were significantly upregulated, while four miRNAs, miR-130b-5p (-5.11-fold regulation), miR-130b-3p (-4.71-fold regulation), miR-7-5p (-3.18-fold regulation), and miR-20b-5p (-3.66-fold regulation), were significantly downregulated (Fig. 5 A). In the late OA vs. control group, 38 miRNAs exhibited significant downregulation (Fig. 5 B), with miR-130b-5p (-65.84-fold regulation), miR-10b-5p (-33.22-fold regulation), miR-379-5p (-32.76-fold regulation), miR-455-5p (-25.15-fold regulation) and miR-148b-3p (-25.06-fold regulation) among the top five downregulated miRNAs. The late OA vs. early OA comparison revealed the downregulation of 40 miRNAs (Fig. 5 C), highlighting the extent of molecular dysregulation in late OA cartilage. Among these, miR-10b-5p (-38.69-fold regulation), miR-101-3p (-34.71-fold regulation), miR-148b-3p (-33.9-fold regulation), miR-455-5p (-32.41-fold regulation) and miR-191-5p (-29.3-fold regulation) were the top 5 downregulated miRNAs. The full list of miRNAs and their respective fold changes is provided in Supplementary Data 2, Table S1 . Interestingly, the four miRNAs that were downregulated in the early OA group vs. the control group, miR-130b-5p, miR-130b-3p, miR-7-5p, and miR-20b-5p, showed an even more pronounced decrease in the late OA group (-8.15, -65.84, -22.09, and − 13.38-fold regulation, respectively, vs. control), indicating their possible role in OA progression. The consistent downregulation of these miRNAs across disease stages is further illustrated in the heatmap (Fig. 5 C). These miRNAs are likely crucial regulators of OA progression and represent promising candidates for therapeutic intervention. 3.3 OA-focused In-silico analysis for predicting miRNA target genes revealed candidate miRNAs potentially implicated in OA pathogenesis A total of 13 miRNAs were initially selected for in-silico target prediction. This panel included the top dysregulated miRNAs, miR-130b-3p, miR-10b-5p, miR-379-5p, miR-455-5p, miR-148b-3p, miR-154-5p, miR-411a-3p, let-7g-5p, miR-369-5p, miR-376a-3p, miR-342-3p, as well as the two miRNAs miR-7-5p and miR-20b-5p, which demonstrated a progressive decline in expression with disease advancement (as detailed in the supplementary data 2, Table S1 ). Although miR-130b-3p, miR-379-5p, miR-455-5p, and miR-411a-3p exhibited marked dysregulation with substantial fold changes, these miRNAs were excluded from further in-silico analysis due to prior literature documenting their gene targets and therapeutic roles in OA ( 19 – 21 ). Thus, miRNAs with strong dysregulation but limited existing functional characterisation were chosen, thereby prioritising miRNAs with the potential to reveal novel regulatory mechanisms. The expression profiles of the selected miRNAs in the early OA vs control, late OA vs control and late OA vs early OA are depicted in Fig. 6 A-C, respectively. Further, the In-silico target gene prediction of the selected miRNAs (Supplementary data 3, Table S1 ) revealed that miR-10b-5p, miR-154-5p, miR-369-5p, miR-376a-3p, and miR-342-3p demonstrated poor target prediction when analysed using an OA-focused strategy (Supplementary data 3, Fig. S1 ). These findings suggest that these miRNAs may play a less significant role in OA pathogenesis. In contrast, miR-148b-3p, let-7g-5p, miR-7-5p, and miR-20b-5p emerged as the most promising candidates for further investigation. miR-148b-3p and let-7g-5p were selectively downregulated in the late OA group, indicating that their dysregulation is associated with molecular changes characteristic of advanced disease stages. In-silico target prediction revealed substantial overlap across TargetScan, miRTarBase, and DIANA microT-CDS for both miRNAs. For miR-148b-3p (Fig. 7 A), mapping of these predicted gene targets with the OsteoDIP database yielded 40 OA-associated target genes. Subsequent network and pathway enrichment analyses revealed significant clustering within OA-relevant signalling cascades, notably the FoxO, ErbB, TGF-β, Oxytocin, and PI3K-Akt pathways. Based on network connectivity and pathway relevance, CDKN1A (Cyclin Dependent Kinase Inhibitor 1A), DCP2 (Decapping mRNA 2), ROCK1 (Rho Associated Coiled-Coil Containing Protein Kinase 1), RGMA (Repulsive Guidance Molecule A), SOS2 (SOS Ras/Rac Guanine Nucleotide Exchange Factor 2), ITGB8 (Integrin Subunit Beta 8), NRAS (Neuroblastoma RAS Viral Oncogene Homolog), and TXNIP (Thioredoxin Interacting Protein) were shortlisted as putative OA-relevant targets of miR-148b-3p. A similar integrative approach for let-7g-5p (Fig. 7 B) identified 53 OA-associated targets through OsteoDIP mapping, with protein-protein interaction analysis indicating strong network connectivity. Pathway enrichment further underscored prominent associations with the FoxO and PI3K-Akt signalling pathways, suggesting shared regulatory roles of both the miRNAs in OA pathophysiology. Consequently, CASP3 (Caspase 3), CCND1 (Cyclin D1), CDKN1A (Cyclin Dependent Kinase Inhibitor 1A), BCL2L1 (BCL2 Like 1), CDC25A (Cell Division Cycle 25A), MDM4 (MDM4 Regulator of P53), IGF1R (Insulin Like Growth Factor 1 Receptor), THBS1 (Thrombospondin 1), and TGFBR1 (Transforming Growth Factor Beta Receptor 1) were shortlisted as key target genes of let-7g-5p. miR-7-5p and miR-20b-5p exhibited a progressive downregulation in expression from early to late OA, suggesting their potential involvement in OA progression. Consistent with its progressive downregulation during disease progression, miR-7-5p exhibited substantial target overlap across prediction platforms, with 66 OA-associated genes identified using the OsteoDIP database. Network analysis revealed multiple OA-relevant hub genes, and pathway enrichment analysis indicated significant involvement of the HIF-1, FoxO, Insulin signalling, and Cellular senescence pathways (Fig. 8 A). Accordingly, RAF1 (RAF Proto-Oncogene Serine/Threonine Kinase), RELA(p65) (RELA Proto-Oncogene, NF-κB Subunit), IRS1 (Insulin Receptor Substrate 1), PIK3CD (Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Delta), TGFB2 (Transforming Growth Factor Beta 2), EIF4E (Eukaryotic Translation Initiation Factor 4E), SNCA (Synuclein Alpha), ARRB1 (Arrestin Beta 1), and PIK3R3 (Phosphoinositide-3-Kinase Regulatory Subunit 3) were shortlisted as the most relevant targets of miR-7-5p. Likewise, miR-20b-5p demonstrated extensive target overlap, with 33 OA-associated genes identified via the OsteoDIP database. Network and pathway analyses revealed strong connectivity and enrichment of pathways related to hypoxia signalling and the ErbB signalling pathway (Fig. 8 B). The final shortlisted targets included HIF1A (Hypoxia Inducible Factor 1 Subunit Alpha), PTEN (Phosphatase and Tensin Homolog), STAT3 (Signal Transducer and Activator of Transcription 3), CDKN1A (Cyclin Dependent Kinase Inhibitor 1A), BCL2L11 (BCL2 Like 11), HSPA8 (Heat Shock Protein Family A Member 8), and SQSTM1 (Sequestosome 1). Collectively, these in-silico analyses demonstrate that miR-148b-3p and let-7g-5p are likely involved in molecular mechanisms underlying late-stage OA, whereas miR-7-5p and miR-20b-5p appear to regulate pathways contributing to OA progression. The extensive target overlap, identification of biologically relevant hub genes, and enrichment of OA-associated pathways strongly support the prioritization of these miRNAs for subsequent functional validation. Table 1 summarises the results of the in-silico analysis and highlights the final shortlisted gene targets for all selected miRNAs. Table 1 In-silico target gene prediction results for selected miRNAs. miRNA Total Predicted Gene Targets Top Hub Genes OA Associated Pathways Final Shortlisted Gene targets Common Gene Targets OA Related Genes (OsteoDip) miR-10b-5p 35 16 GATA3, NCOR2, KLF4, ZMYND11, NCOA6, PPARA, TFAP2C, BCL2L11, SRSF1, TRA2B NA NCOR2, KLF4, BCL2L11 miR-148b-3p 80 40 AGO1, TNRC6A, CDKN1B, DCP2, DNMT1, PRKAA1, PATL1, PRNP, TNRC6B, KPNA4 FoxO, ErbB, TGF-beta, Oxytocin, PI3K-Akt signalling pathways CDKN1B, DCP2, ROCK1, RGMA, SOS2, ITGB8, NRAS, TXNIP miR-154-5p 3 3 NA NA PPP1CC, PCGF5, NRBF2 let-7g-5p 100 53 THBS1, CASP3, IGF1R, CCND1, E2F6, CDC25A, MDM4, BCL2L1, CDKN1A, TGFBR1 Relaxin, FoxO, PI3K-Akt signalling pathways CASP3, CCND1, CDKN1A, BCL2L1, CDC25A, MDM4, IGF1R, THBS1, TGFBR1 miR-369-5p 0 0 NA NA NA miR-7-5p 132 66 ARRB1, IRS1, PIK3CD, EGFR, EIF4E, RELA, RAF1, SNCA, TPGS2, CHAMP1 Insulin resistance, HIF-1, Relaxin, FoxO, Cellular senescence signalling pathways RAF1, RELA, IRS1, PIK3CD, TGFB2, EIF4E, SNCA, ARRB1, PIK3R3 miR-376a-3p 13 6 NA NA NA miR-342-3p 34 17 NA NA NA miR-20b-5p 77 33 HIF-1A, PTEN, SMAD5, STAT3, BCL2L11, CDKN1A, HSPA8, DDX5, SQSTM1 HIF-1, ErbB signalling pathways HIF-1A, PTEN, STAT3, CDKN1A, BCL2L11, HSPA8, SQSTM1 Table 2 In-silico target gene prediction results for selected miRNAs. miRNA Total Predicted Gene Targets Top Hub Genes OA Associated Pathways Final Shortlisted Gene targets Common Gene Targets OA Related Genes (OsteoDip) miR-10b-5p 35 16 GATA3, NCOR2, KLF4, ZMYND11, NCOA6, PPARA, TFAP2C, BCL2L11, SRSF1, TRA2B NA NCOR2, KLF4, BCL2L11 miR-148b-3p 80 40 AGO1, TNRC6A, CDKN1B, DCP2, DNMT1, PRKAA1, PATL1, PRNP, TNRC6B, KPNA4 FoxO, ErbB, TGF-beta, Oxytocin, PI3K-Akt signalling pathways CDKN1B, DCP2, ROCK1, RGMA, SOS2, ITGB8, NRAS, TXNIP miR-154-5p 3 3 NA NA PPP1CC, PCGF5, NRBF2 let-7g-5p 100 53 THBS1, CASP3, IGF1R, CCND1, E2F6, CDC25A, MDM4, BCL2L1, CDKN1A, TGFBR1 Relaxin, FoxO, PI3K-Akt signalling pathways CASP3, CCND1, CDKN1A, BCL2L1, CDC25A, MDM4, IGF1R, THBS1, TGFBR1 miR-369-5p 0 0 NA NA NA miR-7-5p 132 66 ARRB1, IRS1, PIK3CD, EGFR, EIF4E, RELA, RAF1, SNCA, TPGS2, CHAMP1 Insulin resistance, HIF-1, Relaxin, FoxO, Cellular senescence signalling pathways RAF1, RELA, IRS1, PIK3CD, TGFB2, EIF4E, SNCA, ARRB1, PIK3R3 miR-376a-3p 13 6 NA NA NA miR-342-3p 34 17 NA NA NA miR-20b-5p 77 33 HIF-1A, PTEN, SMAD5, STAT3, BCL2L11, CDKN1A, HSPA8, DDX5, SQSTM1 HIF-1, ErbB signalling pathways HIF-1A, PTEN, STAT3, CDKN1A, BCL2L11, HSPA8, SQSTM1 Total predicted gene targets, key hub genes from network analysis, OA-related pathways, and shortlisted gene targets identified using an OA-focused strategy. "NA" denotes the absence of significant results from hub gene analysis or Gene Set Enrichment Analysis. Full gene names for all genes are provided in Supplementary Data 3 Total predicted gene targets, key hub genes from network analysis, OA-related pathways, and shortlisted gene targets identified using an OA-focused strategy. "NA" denotes the absence of significant results from hub gene analysis or Gene Set Enrichment Analysis. Full gene names for all genes are provided in Supplementary Data 3 3.4 I n-vitro validation of miR-7-5p gene targets To prioritise candidates for further investigation, we focused on miRNAs associated with disease progression. Among them, miR-7-5p showed a markedly higher degree of downregulation (-22.09-fold) compared to miR-20b-5p (-13.38-fold), prompting functional validation of its predicted gene targets. We performed gain and loss-of-function experiments in SW1353 cells. The cells were transfected with miR-7-5p mimics (50 nM) and inhibitors (100 nM) to evaluate the effect of miR-7-5p modulation on the mRNA expression of its candidate target genes: RELA (p65), RAF-1, TGFB2, SNCA, PIK3CD, IRS-1, EIF4E , and ARRB1 . Following transfection with the miR-7-5p mimic (50 nM), RT-qPCR analysis confirmed a significant increase in miR-7-5p expression (Fig. 9 A) compared to both the non-transfected cells (739.9 fold upregulation, p < 0.001) and the scrambled control (357.2 fold upregulation, p < 0.05). Given the well-established inverse relationship between miRNAs and their target genes, overexpression of miR-7-5p resulted in a significant downregulation of its targets RELA (p65) (5 fold, p < 0.05, Fig. 9 B), RAF-1 (3.3 fold, p < 0.05, Fig. 9 C), PIK3CD (2.9 fold, p < 0.01, Fig. 9 D ), SNCA (4 fold, p < 0.05, Fig. 9 E), EIF4E (2.2 fold, p < 0.05, Fig. 9 F), IRS-1 (2.2 fold, p < 0.05, Fig. 9 G), and ARRB1 (3.8 fold downregulation, p < 0.05, Fig. 9 H) relative to the scrambled control. These results support the regulatory influence of miR-7-5p on multiple OA-associated genes, highlighting their potential involvement in disease-related molecular pathways. Although the transfection with the miR-7-5p inhibitor resulted in a marked suppression of miR-7-5p levels relative to the scrambled control (5-fold downregulation, p < 0.05 at 50 nM and 250-fold downregulation, p < 0.01 at 100 nM), it did not lead to a significant increase in the expression levels of these gene targets (Supplementary Data 4 Fig. S1 ), possibly due to low basal expression of endogenous miR-7-5p in SW1353 cells which restrict the extent to which target gene upregulation can be detected. 4. Discussion This study provides the first comprehensive overview of miRNA expression patterns associated with OA progression in the Dunkin-Hartley guinea pig, an established model of naturally occurring OA ( 15 ). The onset and progression of OA in guinea pigs are more consistent and reproducible with advancing age, reducing variability commonly associated with surgically induced models. Previous studies have shown that this model closely mimics human primary OA, displaying comparable disease progression and histopathological features ( 14 , 22 – 24 ). Consistent with these reports, histopathological evaluation and the semi-quantitative OARSI scoring of knee joint sections in our study also confirmed a progressive increase in OA severity with advancing age. Moreover, the OARSI histological scores of the Dunkin-Hartley guinea pig knee joint cartilage aligned well with the OARSI grading systems used for human OA cartilage ( 25 ). Control animals exhibited scores comparable to OARSI grade 0–1 in humans, indicating normal or minimally altered cartilage. Early OA animals displayed scores corresponding to mild OA (human OARSI grades 2–3), characterised by superficial fibrillation. Late OA animals demonstrated scores consistent with moderate to advanced OA (human OARSI grades 4–5), reflecting deep fissuring, extensive matrix loss, and tidemark duplication. These histopathological findings confirmed that the Dunkin-Hartley guinea pig model reproduces the structural features of human idiopathic OA in a stage-dependent manner. miRNAs function as post-transcriptional regulators of gene expression by targeting the 3′ untranslated regions of mRNAs, leading to either their degradation or translational repression. They play a critical role in maintaining articular cartilage homeostasis, and their dysregulation has been implicated in cartilage degradation, inflammation, and aging ( 26 ). In our present study, we profiled the expression of 100 selected miRNAs in the cartilage of Dunkin-Hartley guinea pigs at different stages of OA progression using RT-qPCR arrays. To enable miRNA profiling from micro-quantities of guinea pig cartilage tissue, the RNA isolation protocol was optimised for downstream RT-qPCR applications as reported previously ( 16 ). While RNA purity and concentration were satisfactory, the RIN values were suboptimal for application in high-throughput transcriptomic platforms. Such reduced RIN values are commonly observed in cartilage due to its dense ECM, low cellularity, and high RNase activity ( 27 ). Despite these limitations, the RNA was of adequate quality for RT-qPCR-based quantification of miRNAs, which is less sensitive to RNA fragmentation, thereby providing a cost-effective and practical strategy for targeted miRNA profiling under conditions of limited tissue availability. These miRNAs were shortlisted based on their potential involvement in OA pathophysiology, as suggested by prior literature and predicted expression in bone. We identified a total of 37 miRNAs to be significantly downregulated in the late OA group, with miR-130b-3p, miR-10b-5p, miR-379-5p, miR-455-5p, and miR-148b-3p as the top five most downregulated miRNAs. Notably, several of these downregulated miRNAs have previously been implicated in cartilage regeneration, chondrocyte proliferation, and OA attenuation. For instance, miR-130b-3p and miR-455 have been identified in the cargo of extracellular vesicles (EVs) isolated from synovial mesenchymal stem cells (SMSCs) and bone marrow mesenchymal stem cells (BMSCs), respectively. When administered intra-articularly in surgically induced OA models, these EVs significantly alleviated disease severity by modulating the AKT/β-catenin pathway in the case of miR-130b-3p and the FOXO pathway in the case of miR-455. ( 19 , 21 ). In another study, miR-130b-3p was reported to be the key upregulated miRNA in the EVs isolated from the synovial fluid of OA patients, suggesting its role in cell-to-cell communication in OA cells ( 28 ). Moreover, miR-130b-3p has also been reported to be involved in chondrogenic differentiation, further supporting its regulatory role in cartilage biology ( 29 , 30 ). In line with our findings, Zhang et al. recently reported the downregulation of miR-455-5p in human OA cartilage and IL-1β-treated chondrocytes ( 31 ), while Min et al. had earlier shown reduced levels of miR-455-5p during cartilage development in bone matrix gelatin (BMG) rat model ( 32 ). Moreover, intra-articular injection of miR-455-5p mimics in a surgically induced OA model significantly inhibited cartilage destruction ( 33 ). Similarly, the observed downregulation of miR-379-5p in our study aligns with prior findings in IL-1β-treated primary chondrocytes alongside human OA cartilage, where its expression was also reduced ( 34 ). In vivo administration of miR-379-5p agomir has been shown to enhance cartilage repair, further supporting its chondroprotective role. Contrary to this, the roles of top-downregulated miRs in our study, miR-10b-5p and miR-148b-3p, remain largely unexplored in OA. miR-148b-3p has been documented as a tumor suppressor in osteosarcoma ( 35 ) and a promoter of osteogenesis ( 36 ), while circulating miR-10b-5p levels have been linked to osteoporosis, indicating its involvement in bone metabolism ( 37 , 38 ). Both the miRNAs were also associated with rheumatoid arthritis (RA) pathogenesis ( 39 , 40 ). Together, these studies suggest shared regulatory roles for these miRNAs across joint diseases, making their further exploration in OA both relevant and compelling. Furthermore, miR-130b-3p, miR-7-5p, miR-20b-5p, and miR-130b-5p were notably downregulated in both early and late OA groups compared to controls, suggesting progressive suppression of regulatory miRNAs as OA advances. miR-7-5p, which is likely to be involved in OA progression, has also been linked to osteosarcoma, osteoporosis ( 41 – 43 ), and proposed as a diagnostic biomarker for RA ( 44 ). While we found miR-7-5p downregulated in cartilage during spontaneous OA progression, prior studies have reported context-dependent roles for miR-7-5p in OA pathogenesis. Previous in vitro studies have reported upregulated miR-7-5p expression under inflammatory OA conditions, largely attributed to reduced sequestration by regulatory non-coding RNAs, including the circular RNA ciRS-7 and the lncRNA ZFAS1 ( 45 , 46 ). Another study reported increased miR-7-5p expression in a rat OA model, where elevated miR-7-5p impaired chondrocyte autophagy by targeting ATG4A, contributing to cartilage degeneration ( 47 ). Notably, a previous study reported reduced expression of miR-7-5p in OA cartilage and demonstrated that overexpression of miR-7-5p in ATDC5 cells enhanced cartilage proteoglycan content while preventing chondrocyte hypertrophy ( 48 ). Another study has reported its downregulation in the serum of OA patients ( 49 ). These discrepancies likely reflect tissue and model-specific dynamics and also the presence of competing non-coding RNAs. Our findings may capture early intrinsic changes unique to spontaneous OA. Thus, miR-7-5p remains a biologically relevant candidate for further investigation. In addition, previous studies have also highlighted miR-20b-5p as a regulatory miRNA with potential protective roles in OA pathogenesis. Through integrative bioinformatic analyses, miR-20b-5p was identified as a significantly dysregulated miRNA within OA-associated regulatory networks and a key node capable of modulating multiple OA-relevant targets ( 50 ). Experimental evidence further demonstrated that miR-20b-5p regulates angiogenesis in cartilage and subchondral bone in an early-stage OA rat model, with its dysregulation promoting aberrant vascular invasion and subchondral bone remodelling, thereby contributing to cartilage degeneration ( 51 ). The consistent downregulation of miRNAs observed in aging cartilage in our study likely reflects a progressive loss of cartilage homeostasis, driven primarily by suppression of miRNA expression rather than selective biogenesis of miRNAs. Advancing OA is associated with chondrocyte senescence and apoptosis, which can impair miRNA biogenesis and contribute to widespread miRNA downregulation. Furthermore, as primary OA is an age-related disease, aging cartilage also exhibits epigenetic alterations, including DNA methylation of miRNA promoters and histone modifications, leading to transcriptional silencing of miRNAs ( 52 – 54 ). These effects may preferentially impact miRNAs involved in cartilage homeostasis and tissue maintenance, thereby explaining the predominance of downregulated miRNAs with disease progression. This may contribute to enhanced expression of catabolic and inflammatory genes. To strengthen the biological relevance of our miRNA profiling results and to identify candidate miRNAs with potential therapeutic value, miRNAs with strong dysregulation but limited existing functional characterization were chosen, thereby prioritizing miRNAs with the potential to reveal novel regulatory mechanisms. We conducted an OA-focused in silico analysis on the top-selected miRNAs (miR-10b-5p, miR-148b-3p, miR-154-5p, let-7g-5p, miR-369-5p, miR-7-5p, miR-376a-3p, miR-342-3p, and miR-20b-5p). For this, OsteoDIP database ( 18 ), a curated platform specific to osteoarthritis research, was used to improve the specificity of OA-related target prediction. Additionally, gene set enrichment analysis (GSEA) was employed to identify perturbed molecular functions and pathways, while hub gene network analysis provided an overview of the genes most closely associated with the disease. Such integrated in silico approaches have previously proven valuable in OA research, offering a bird’s-eye view of transcriptional regulation and helping prioritize miRNAs with functional and therapeutic relevance ( 55 ). Interestingly, although miR-10b-5p was among the most downregulated miRNAs and was previously implicated in joint diseases, its predicted gene targets and associated OA-relevant pathways did not show strong support for a direct role in OA pathogenesis. Similarly, miR-154-5p, miR-376a-3p, miR-342-3p, and miR-369-5p also lacked compelling pathway associations and subsequent hub genes in this context. Based on the present in-silico results, miR-148b-3p, let-7g-5p, miR-7-5p, and miR-20b-5p demonstrated better prospects among all identified miRNA-mRNA regulatory networks and pathways. Among late OA-specific miRNAs, miR-148b-3p and let-7g-5p were predicted to be involved in the FOXO and PI3K-Akt signalling pathways, both of which are known to influence chondrocyte survival and cartilage matrix homeostasis ( 56 , 57 ). miR-7-5p and miR-20b-5p, which were found to be involved in OA progression, were predicted to regulate the HIF-1 signaling pathway, a key contributor to OA pathology ( 58 – 61 ). Additionally, miR-7-5p was associated with several well-established OA-related pathways, including insulin signaling, FOXO signaling, relaxin signaling, and cellular senescence regulation, supporting its potential role in cartilage degeneration and joint aging ( 62 – 68 ). Together, our in silico analysis highlights the miRNAs with plausible roles in OA pathogenesis and supports their prioritization for functional validation. However, further experimental studies are needed to elucidate their precise biological mechanisms. Given the potential of miRNAs to reveal early molecular events underlying idiopathic OA, where triggers are still poorly defined, their identification is crucial for developing targeted, miRNA-based therapies. In our study, miR-7-5p emerged as a key miRNA associated with OA progression with a greater fold change than miR-20-5p. Furthermore, its predicted association with multiple OA pathways and established involvement in inflammatory processes ( 69 ), miR-7-5p emerged as a compelling candidate for in vitro investigation. The upregulation of miR-7-5p in SW1353 cells led to the downregulation of its target genes RELA (p65), RAF-1, TGFB2, SNCA, PIK3CD, IRS-1, EIF4E, and ARRB1 , highlighting the regulatory role of miR-7-5p. RELA (p65) , a central NF-κB subunit, drives OA pathogenesis by promoting catabolic enzymes like ADAMTS5 and MMPs ( 70 , 71 ). Knockout and inhibition studies in vitro and in vivo show that silencing RELA reduces cartilage damage and chondrocyte apoptosis ( 72 ) ( 73 , 74 ). RAF-1 plays a key role in mediating inflammatory responses in OA ( 75 ). Additionally, its co-activation with RELA (p65) has been shown to enhance MMP-3 production in osteoarthritic synovial fibroblasts (OASFs) via the MAPK/ERK pathway ( 76 ). Contrary to this, TGFB2 plays a context-dependent role in OA and regulates early chondrogenesis ( 77 ). Although it is chondroprotective at physiological levels, excessive TGF-β signalling may contribute to synovial fibrosis and osteophyte formation. Thus, maintaining balanced, tissue-specific activation is critical ( 78 ). SNCA , primarily studied in neurodegenerative disorders ( 79 ), showed downregulation in our study, hinting at a possible OA-neurodegeneration link. This can be explored further, given the reported co-occurrence of OA and neurodegenerative conditions in aging populations ( 80 ). The gene target IGF-1 is a key factor for the maintenance of bone homeostasis ( 81 ), and modulations in the IGF-1/AKT/IRS-1 signalling axis have been associated with OA pathology ( 82 , 83 ). Although PIK3CD, EIF4E , and ARRB1 remain less characterized in OA ( 84 , 85 ), their known roles in PI3K/AKT signalling, translational control, and GPCR desensitization suggest potential relevance and merit further functional investigation. Several validated targets, RELA, RAF-1, SNCA, TGFB2 , and IRS-1 , have been previously confirmed as miR-7-5p targets via luciferase and RNA immunoprecipitation (RIP) assays in other diseases, reinforcing our findings ( 86 – 95 ). This strengthens the robustness of the bioinformatic strategy employed for gene target prediction. In our study, miR-7-5p inhibition did not markedly alter target gene levels, likely owing to its low expression in SW1353 cells. The observation suggests that these cells are better suited for miR-7-5p gain-of-function studies. More robust knockdown strategies, like CRISPR-Cas or shRNA may yield clearer outcomes in future investigations. Our findings highlight the utility of RT-qPCR array-based profiling as a sensitive tool for identifying miRNA alterations, even in microquantities of cartilage tissue. This study offers a comprehensive overview of miRNA dysregulation in idiopathic OA and reinforces the translational relevance of the Dunkin-Hartley guinea pig model for investigating OA pathogenesis and evaluating pharmacological and non-pharmacological interventions. The identified downregulated miRNAs emerge as promising candidates for therapeutic exploration and for deciphering the epigenetic regulation of OA. Additionally, exploring these downregulated miRNAs as a therapeutic panel may offer translational potential. However, in contrast to their well-established role as diagnostic biomarkers, miRNA-based combination therapies remain largely conceptual and face substantial challenges that must be addressed before clinical application. The present study has certain limitations that should be acknowledged. Although the use of SW1353 cells, a chondrosarcoma cell line, provides a convenient experimental system, it has limited ability to fully mimic the OA chondrocyte phenotype. Furthermore, the lack of validation of candidate miRNAs in human OA cartilage samples is a limitation, and future studies involving human tissues will be necessary to enhance translational relevance. Declarations Competing Interests All authors declare that they have no conflict of interest. Author Contributions Nidhi Bhardwaj: Writing- original draft, validation, methodology, investigation, formal analysis, data curation, conceptualization. Diksha Rana: Methodology, investigation, supervision, and formal analysis. Alka Bhatia: supervision, resources, validation and formal analysis. Uttam Chand Saini: supervision and resources. Jyotdeep Kaur: project administration, supervision, research funding, methodology, conceptualization, writing- review and editing. Ethics Approval All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. The research reported in this article was performed with approval of the Institutional Animal Ethics Committee (108/100/99/IAEC/688) of Postgraduate Institute of Medical Education and Research (PGIMER). Clinical Trial Number Not Applicable Funding We gratefully acknowledge the Indian Council of Medical Research (ICMR) for funding the study (Grant No. 5/4–5/3/1/Ortho/2019/NCD-1), which supported the experimental work. Author Contribution Nidhi Bhardwaj: Writing- original draft, validation, methodology, investigation, formal analysis, data curation, conceptualization. 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Additional Declarations No competing interests reported. Supplementary Files SupplementaryData1.docx SupplementaryData2.docx SupplementaryData3.docx SupplementaryData4.docx GraphicalAbstract.jpeg Graphical Abstract Graphical representation and workflow illustrating dysregulated miRNAs in cartilage and highlighting miR-7-5p as a key regulator of idiopathic osteoarthritis progression. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8914957","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611223402,"identity":"3894334f-8d6a-4049-aa6c-6e55c776a54d","order_by":0,"name":"Nidhi Bhardwaj","email":"","orcid":"","institution":"Post Graduate Institute of Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Bhardwaj","suffix":""},{"id":611223403,"identity":"7fe0dd6e-6703-4d8a-ab2a-f0c5323af307","order_by":1,"name":"Diksha Rana","email":"","orcid":"","institution":"Post Graduate Institute of Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Diksha","middleName":"","lastName":"Rana","suffix":""},{"id":611223404,"identity":"210ecf07-771c-4c5d-9b50-4a199162903c","order_by":2,"name":"Alka Bhatia","email":"","orcid":"","institution":"Post Graduate Institute of Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Alka","middleName":"","lastName":"Bhatia","suffix":""},{"id":611223405,"identity":"6516f006-b071-4d3b-80d2-16121056a693","order_by":3,"name":"Uttam chand Saini","email":"","orcid":"","institution":"Post Graduate Institute of Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Uttam","middleName":"chand","lastName":"Saini","suffix":""},{"id":611223406,"identity":"6ca5ca70-f110-4be1-8a2c-adc8b8e8229a","order_by":4,"name":"Jyotdeep Kaur","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3NsUoDMRjA8YRAbzm5NTf1Fa6bQq2vciHQqb3lQAqKRgLpEtAxhT6Ej5AQuC7FuaOTU4eMDqLmTsEuaTsWzB/yZcj3IwDEYicYZH6k/mQIMu0AKH8f8D4CWUvyOTdGHUM61ZJi3VCb/pFwSNHV63Y2rMBmUthLMaqyhEHnwHkV/EKNycNyPa6h8mQqaJ1LjXIFcB0mkwE/E5Zw3BFEnjdlD6UAE7aXfH4R0ZILcX8sYZrItCkt9N8dJvKNLJYNrXHCtZEvK7KQhueqCJPBnGq3vR1VVxZy9359Qx4Tbpyb3YXJU9ndPxP2uumXi8C+r5/pHQI+wpuxWCz2f/sGxxliII/bFowAAAAASUVORK5CYII=","orcid":"","institution":"Post Graduate Institute of Medical Education and Research","correspondingAuthor":true,"prefix":"","firstName":"Jyotdeep","middleName":"","lastName":"Kaur","suffix":""}],"badges":[],"createdAt":"2026-02-19 07:24:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8914957/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8914957/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105600840,"identity":"1bc957e9-ff65-4739-b72c-b71590a96859","added_by":"auto","created_at":"2026-03-27 19:48:26","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3697458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe figure illustrates the identification and screening of miRNAs related to osteoarthritis (OA) in humans and Dunkin-Hartley guinea pigs\u003c/strong\u003e. A total of 2,654 mature \u003cem\u003eHomo sapiens\u003c/em\u003e miRNAs and 656 \u003cem\u003eCavia porcellus \u003c/em\u003e(guinea pig) miRNAs were retrieved from the miRBase database, revealing 503 miRNAs common to both species. From previously published reports, 52 miRNAs were identified as associated with OA. Additionally, 41 miRNAs highly expressed in bone were fetched from the human miRNA tissue atlas, and seven miRNAs were found to be differentially expressed (DE) in the synovial fluid of OA patients based on microarray data from the lab. These 100 miRNAs were selected for profiling via RT-qPCR array, allowing for targeted investigation of their roles in OA pathogenesis.\u003c/p\u003e","description":"","filename":"Fig1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/fb96588730bbe1bdf6c331e5.jpeg"},{"id":105600830,"identity":"73b207c7-d615-41f9-8f04-580943e99e51","added_by":"auto","created_at":"2026-03-27 19:48:26","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3439876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn-silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e analysis strategy for identifying miRNA target genes in knee OA.\u003c/strong\u003e\u003cbr\u003e\nTarget genes of selected dysregulated miRNAs were predicted using TargetScanHuman, miRTarBase, and DIANA-microT-CDS. The common gene targets were analyzed to ensure biological relevance. Identified gene targets were analyzed via STRING for PPI network construction and hub gene identification using Cytoscape. Pathway enrichment analysis was performed using KEGG via ShinyGO, and OA-related genes were filtered using the OsteoDIP database. Candidate genes for each miRNA were shortlisted by selecting those that appeared in at least two of the following categories: hub genes, OA pathway-associated genes, and OA-related genes from OsteoDIP.\u003c/p\u003e","description":"","filename":"Fig2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/19331149af6c78399b3986c0.jpeg"},{"id":105728802,"identity":"26138528-882e-40fa-95d6-b0c3e1e4f650","added_by":"auto","created_at":"2026-03-30 11:12:45","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":302649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMacroscopic and\u003c/strong\u003e \u003cstrong\u003ehistopathological assessment of knee cartilage in Dunkin-Hartley guinea pigs to evaluate OA severity.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Macroscopic images of the tibial plateau show progression from smooth cartilage in control animals to mild surface irregularities in early OA animals and severe erosion exposing subchondral bone in late OA animals. \u003cstrong\u003e(B)\u003c/strong\u003e Representative H\u0026amp;E-stained sections showing cartilage structure (CS) at 20X (Scale bar: 200 µm), cellularity (CL) and tidemark (TM) at 40X magnification (Scale bar: 100 µm). Control animals exhibited smooth articular cartilage, normal cellularity and intact tidemark. Early OA animals showed mild cartilage surface irregularities and hypercellularity but intact tidemark. Late OA animals displayed severe surface disruption, chondrocyte hypercellularity, and clustering along with tidemark duplication.\u003c/p\u003e","description":"","filename":"Fig3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/27428a730b24974644c6ca8c.jpeg"},{"id":105600834,"identity":"e990798c-c798-4475-8dad-d901f0db2075","added_by":"auto","created_at":"2026-03-27 19:48:26","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":226100,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssessment of proteoglycan content and overall OARSI scoring of the articular cartilage in Dunkin-Hartley guinea pigs (A)\u003c/strong\u003e Safranin-O-fast green and toluidine blue staining (20X) indicated uniform proteoglycan content (PC) in the control group, with significant proteoglycan loss (high OARSI scores) in the late OA group. Scale bar: 200 µm. \u003cstrong\u003e(B)\u003c/strong\u003e OARSI scoring of articular cartilage across age groups, analyzed using one-way ANOVA with Tukey’s post hoc test. ***p \u0026lt; 0.001 and ****p \u0026lt; 0.0001 vs. control; ###p \u0026lt; 0.001 vs. early OA.\u003c/p\u003e","description":"","filename":"Fig4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/81c0c782fb3fa299e6bf9128.jpeg"},{"id":105728259,"identity":"dc676a2a-c830-4084-a967-20f1be9a0a7d","added_by":"auto","created_at":"2026-03-30 11:11:09","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":403523,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of dysregulated miRNAs in the cartilage of Dunkin-Hartley guinea pigs. \u003c/strong\u003eVolcano plots illustrating the differential expression of miRNAs in \u003cstrong\u003e(A)\u003c/strong\u003e early OA vs. control, \u003cstrong\u003e(B)\u003c/strong\u003elate OA vs. control, and \u003cstrong\u003e(C)\u003c/strong\u003e late OA vs. early OA comparisons. Significantly upregulated miRNAs are shown in red, downregulated miRNAs in green, with significance set at p \u0026lt; 0.05. \u003cstrong\u003e(D) \u003c/strong\u003eHeat maps illustrating the expression profiles of dysregulated miRNAs across early OA, late OA, and control groups. The colour gradient from red to green indicates miRNA expression levels, with red representing upregulation and green representing downregulation. miR-130b-3p, miR-7-5p, miR-20b-5p, and miR-130b-5p, which might be potentially involved in OA progression, are highlighted.\u003c/p\u003e","description":"","filename":"Fig.5jpeg.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/92216e40afc93fa34534a4ba.jpeg"},{"id":105600841,"identity":"a5c53a6b-7303-4e45-ba21-c86d200a1c77","added_by":"auto","created_at":"2026-03-27 19:48:26","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":96247,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plots illustrating the downregulated expression of selected miRNAs \u003c/strong\u003ein \u003cstrong\u003eA)\u003c/strong\u003e Early OA vs. Control, \u003cstrong\u003eB)\u003c/strong\u003e Late OA vs. Control and \u003cstrong\u003eC) \u003c/strong\u003eLate OA vs. Early OA based on the fold change of ≥ 2.0 and p-value ≤ 0.05 cut off.\u003c/p\u003e","description":"","filename":"Fig.6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/d953aa8daa8817afa3051d0a.jpeg"},{"id":105600832,"identity":"7f2c90b0-d5d8-4ac1-8912-eee7a391d0bb","added_by":"auto","created_at":"2026-03-27 19:48:26","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":583008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative analysis of the OA-focused \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein-silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003etarget gene prediction of late OA-specific miRNAs (A) miR-148b-3p and (B) let-7g-5p.\u003c/strong\u003e A Venn diagram shows the overlap of predicted gene targets among TargetScan, miRTarBase, and Diana microT-CDS databases. The network analysis highlights key hub genes and significantly enriched pathways, with osteoarthritis-associated pathways marked in red. The fold enrichment and -log10(FDR) values are shown in the bar graphs to indicate pathway significance.\u003c/p\u003e","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/a0f467cc4f76b4f906b54342.jpg"},{"id":105728673,"identity":"a4c662ee-c98d-4c82-81f6-92064b23736e","added_by":"auto","created_at":"2026-03-30 11:12:26","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":555042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative analysis of the common predicted targets, hub genes, and enriched pathways for (A) miR-7-5p and (B) miR-20b-5p potentially involved in OA progression.\u003c/strong\u003e Venn diagram shows the overlap of predicted gene targets among TargetScan, miRTarBase, and Diana microT-CDS databases. The network analysis highlights PPI and key hub genes predicted using cytoscape. \u0026nbsp;Pathway analysis depicts significantly enriched pathways, with osteoarthritis-associated pathways marked in red. The fold enrichment and -log10(FDR) values are shown in the bar graphs to indicate pathway significance.\u003c/p\u003e","description":"","filename":"Fig.8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/7f2d45b8c18b30e8179bbae7.jpg"},{"id":105600833,"identity":"94c8f1af-4ebd-4d45-aeb5-845a36db303f","added_by":"auto","created_at":"2026-03-27 19:48:26","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":148263,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative mRNA expression of A) miR-7-5p and its target genes B) \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eRELA \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(p65), C) \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eRAF1, \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eD) \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePIK3CD,\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e E)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e SNCA,\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e F) \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEIF4E,\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eG) \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIRS1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and H) \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eARRB1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in SW1353 cells following transfection with miR-7-5p mimics (50 nM).\u003c/strong\u003eThe expression values were normalized to either the non-transfected control or the scrambled control. Data are presented as mean ± SEM, and statistical significance was assessed using an unpaired t-test.. * p \u0026lt; 0.05, ** p \u0026lt; 0.01 and *** p \u0026lt; 0.001 and versus the non-transfected control or the scrambled control.\u003c/p\u003e","description":"","filename":"Fig9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/e2bc653d07ab9e5d3c7da6ba.jpeg"},{"id":105752175,"identity":"75e2615d-73a6-46e0-8506-467aaa8f9b3c","added_by":"auto","created_at":"2026-03-30 15:55:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11301654,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/9d0454ee-fe8a-4e7b-b4d4-7b42886de314.pdf"},{"id":105729027,"identity":"839f88ed-76f5-4d6b-be6d-48c4ddc63559","added_by":"auto","created_at":"2026-03-30 11:13:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2079037,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/f75f58ae0f3e7f9bea9783b8.docx"},{"id":105600843,"identity":"90d4a09d-cc98-4b40-8b51-bcb03e40d925","added_by":"auto","created_at":"2026-03-27 19:48:29","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19778,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/bd3bc54348910884b6f1e9c6.docx"},{"id":105728038,"identity":"96b325f4-9368-4a30-a808-0d5a5550394d","added_by":"auto","created_at":"2026-03-30 11:08:32","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3547192,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/c6d56b4beeba47060ca6866e.docx"},{"id":105728077,"identity":"3a0ec4f5-62d8-4dfe-9fd4-e583dca5c719","added_by":"auto","created_at":"2026-03-30 11:09:10","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":250474,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/521662be12c6b594da806374.docx"},{"id":105600838,"identity":"076cdf03-b662-4112-a4cf-8c44ca3619bd","added_by":"auto","created_at":"2026-03-27 19:48:26","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":794870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraphical representation and workflow illustrating dysregulated miRNAs in cartilage and highlighting miR-7-5p as a key regulator of idiopathic osteoarthritis progression.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GraphicalAbstract.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8914957/v1/88eacb4115d640d167756feb.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive microRNA profiling of aging cartilage in the Dunkin-Hartley guinea pig model identifies key regulators of osteoarthritis progression","fulltext":[{"header":"Key Message","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eWhat is already known on this topic:\u003c/strong\u003e Osteoarthritis lacks effective disease-modifying therapies, and aging-related idiopathic OA remains poorly captured by existing animal models.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWhat this study adds:\u003c/strong\u003e The Dunkin-Hartley guinea pig model of idiopathic OA reveals age-dependent downregulation of cartilage miRNAs.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHow this study might affect research, practice or policy:\u003c/strong\u003e miR-7-5p acts as a central regulator of OA pathogenesis and emerges as a potential therapeutic target, supporting the development of miRNA-based disease-modifying therapies beyond symptom management.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eOsteoarthritis (OA) is the prevailing reason for joint disability, particularly in the knee joint (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Certain ethnic and racial groups, particularly older individuals, women, and individuals with lower socioeconomic status, bear a heavier burden of the condition (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although OA is most closely correlated with aging, joint injury, obesity, occupation-related factors and mechanical stresses are also responsible for the progression of OA (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Despite its widespread impact, OA remains a complex, multifactorial disease with poorly understood molecular mechanisms. Current treatments primarily focus on symptom relief, and there is a critical unmet need for disease-modifying therapies that can halt or reverse OA progression.\u003c/p\u003e \u003cp\u003eMicroRNAs (miRNAs) function as key players in post-transcriptional gene regulation, influencing multiple biological pathways, including cartilage homeostasis and inflammation (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These small non-coding RNAs modulate the stability and translation of target mRNAs, thereby influencing chondrocyte function, extracellular matrix (ECM) turnover, and inflammatory responses. Dysregulation of specific miRNAs, such as miR-140 and miR-146a, has been consistently reported in OA (\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), illustrating their potential to serve not only as biomarkers for clinical diagnosis but also as intervention targets. Several miRNA-based therapies are currently in clinical trials for various diseases (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, none have yet been developed specifically for OA. Given their central regulatory roles, miRNAs offer promising avenues for developing novel epigenetic-based therapies aimed at restoring joint homeostasis. However, despite growing interest, significant gaps remain in understanding the dynamic miRNA changes during the natural course of OA, especially in age-related idiopathic cases where disease triggers are not well defined. Most human studies rely on late OA tissue samples, which may not accurately capture early molecular events driving OA pathogenesis. Additionally, many animal models focus on surgically induced OA, which does not fully replicate the age-related spontaneous progression observed in humans. These limitations underscore the need for profiling miRNA expression in models that better mimic the natural onset and progression of idiopathic OA.\u003c/p\u003e \u003cp\u003eThe Dunkin-Hartley guinea pig model is widely recognised for its close resemblance to human primary OA, developing spontaneous cartilage degeneration and histopathological features with age (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This animal model is, therefore, translationally relevant for studying early disease mechanisms and evaluating therapeutic strategies. In this study, we employed the Dunkin-Hartley guinea pig model and, for the first time, studied the PCR array-based profiling to identify miRNAs dysregulated during spontaneous OA progression. The advancement in RT-qPCR array technology enabled the sensitive profiling of miRNAs from limited cartilage tissue, facilitating the discovery of key regulatory miRNAs involved in OA progression.\u003c/p\u003e \u003cp\u003eOur approach aimed to capture dysregulated miRNAs during the disease progression that may contribute to OA pathogenesis. By employing a comprehensive OA-focused \u003cem\u003ein silico\u003c/em\u003e approach, we identified key miRNAs with potential regulatory roles in OA. We also conducted \u003cem\u003ein vitro\u003c/em\u003e experiments to validate the regulatory influence of candidate miRNA on their target genes. Focusing specifically on age-associated idiopathic OA and utilising a relevant animal model, this study offers an insightful overview of miRNAs' involvement in the progression of the disease and lays the foundation for future miRNA-based therapeutic strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental animals and articular cartilage collection\u003c/h2\u003e \u003cp\u003eMale Dunkin-Hartley guinea pigs (\u003cem\u003eCavia porcellus\u003c/em\u003e) were obtained after ethical clearance from the Institute Animal Ethics Committee (IAEC) of Postgraduate Institute of Medical Education and Research, India. All animals were housed in standard stainless-steel guinea pig drawer-style trolleys (36'' x 23'' x 9'') in pairs and had free access to certified guinea pig diet (Hindustan Animal Feeds, Guinea Pig Grower Feed) and tap water. Thirteen guinea pigs were allocated into three groups for grade-wise analysis on the basis of their age and weight. The 3-month-old guinea pigs (283 \u0026plusmn; 25 g) served as the control group (n\u0026thinsp;=\u0026thinsp;4), while the 7-month-old guinea pigs (600 \u0026plusmn; 10 g) represented the early OA group (n\u0026thinsp;=\u0026thinsp;4) and the 12-month-old guinea pigs (835 \u0026plusmn; 15 g ) represented the late OA group (n\u0026thinsp;=\u0026thinsp;5). The animals were euthanized with lethal doses of ketamine: xylazine (85 mg/kg: 15 mg/kg), and the knee joints were harvested. For histological examination, one knee joint was fixed in 10% neutral buffered formalin (NBF), while the other joint was dissected on ice and the connective tissue and muscle was removed. Articular cartilage was then scraped from the femoral and tibial condyles using a surgical blade.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 RNA isolation and miRNA profiling\u003c/h2\u003e \u003cp\u003eThe cartilage samples flash-frozen using liquid nitrogen were homogenized using a mini-beadbeater-16 (Biospec, Cat. no. 607), and total RNA was isolated using the Quick-RNA\u0026trade; Miniprep Plus Kit (Zymo Research, Cat. no. R1057) with some modifications as previously demonstrated (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The RNA concentration, RNA quality, A\u003csub\u003e260:280\u003c/sub\u003e, and A\u003csub\u003e260:230\u003c/sub\u003e were determined spectrophotometrically (Supplementary Data 1, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) using the NanoDrop\u0026reg;-ND 1000 spectrophotometer. RNA was then reverse transcribed using miRCURY LNA RT Kit (Qiagen, Cat. no. 339340) for miRNA cDNA synthesis. RT-qPCR arrays provide the means to profile hundreds of miRNAs in a single experiment. Pre-dispensed miRNA-specific primer sets were customized in 384-well plates for SYBR Green-based detection using miRCURY LNA Custom PCR Panel (Qiagen, Cat. no. 339330). Expression of 100 miRNAs was analyzed in cartilage samples from the control, early OA (n\u0026thinsp;=\u0026thinsp;4), and late OA group (n\u0026thinsp;=\u0026thinsp;5). A total of 2 ng of RNA was utilized in each well of the PCR array plate for a 10 \u0026micro;L SYBR Green-based RT-qPCR reaction performed in duplicates (Qiagen, Cat. no. 339345). The transcripts were quantified using the following program: initial hot start for 2 min at 95\u0026deg;C, followed by 40 cycles of 95\u0026deg;C for 10 s, 56\u0026deg;C for 1 min (CFX384 Touch Real-Time PCR Detection System BIORAD). The plates were inspected by melting curve analysis after amplification (Supplementary Data 1 Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-\u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e) to confirm a single PCR product in each well. The raw Ct values were used for relative quantification using the GeneGlobe Data Analysis software (Qiagen), and the threshold cycle (Ct) cut-off was set to 38. The Ct values of target miRNAs were normalized using the global Ct mean of expressed miRNAs method, and the differentially expressed miRNAs with p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and fold change\u0026thinsp;\u0026ge;\u0026thinsp;2.0 were selected for further analysis. The miRNA dysregulation was expressed as fold regulation, representing the relative change in expression compared to the control group. Negative values indicated downregulation, whereas positive values indicated upregulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 miRNA selection strategy for RT-qPCR arrays\u003c/h2\u003e \u003cp\u003eA subset of 100 miRNAs was chosen for miRNA profiling using RT-qPCR arrays (Supplementary Data 1, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). A combination of database mining, literature review, and laboratory data was used to identify miRNAs potentially involved in OA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The \u003cem\u003eHomo sapiens\u003c/em\u003e mature miRNAs were retrieved from the miRBase database, totalling 2,654 miRNAs. Similarly, \u003cem\u003eCavia porcellus\u003c/em\u003e (guinea pig) miRNAs were also fetched from miRBase, comprising 656 miRNAs. This was followed by mapping the miRNomes of the two species, which revealed 503 common miRNAs shared between the species. These miRNAs were further screened on the basis of three filters: (i) miRNAs reported in OA, (ii) bone-expressed miRNAs, and (iii) miRNAs differentially expressed in synovial fluid samples from OA patients, based on previous microarray data from our lab. Literature mining revealed the potential involvement of 52 miRNAs associated with OA. The identification of miRNAs exhibiting high expression in bone was done using the human miRNA tissue atlas, and a total of 41 miRNAs were selected based on their tissue specificity index (TSI) of \u0026ge; 0.75. The TSI ranges from 0 to 1, with lower values (close to 0) reflecting ubiquitous expression of miRNAs and higher values (close to 1) indicating tissue-specific expression. The comparison with the previous miRNA microarray data of our lab in synovial fluid samples from OA patients revealed 7 common miRNAs, which were also included for miRNA profiling in the cartilage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Histology and OARSI scoring\u003c/h2\u003e \u003cp\u003eThe knee joints were routinely fixed with 10% NBF for 20 days and then decalcified with 5% nitric acid for 4 to 6 weeks at room temperature (22 \u0026plusmn; 5\u0026deg;C), changing the solution weekly. The knee joints were sectioned coronally and placed in cassettes, followed by rinsing the specimens with water before being embedded in paraffin. Each sample was then coronally sectioned using a microtome (Slee, Cut 5062) at a thickness of 6 \u0026micro;m. The sections were subsequently dewaxed in xylene and hydrated using a graded series of ethanol before undergoing staining with (H\u0026amp;E) haematoxylin and eosin (Sigma, Cat. No. H9627) for the general assessment of tissue morphology. We also performed Safranin O-Fast green (Sigma, Cat No. S2255) and toluidine blue (Sigma, Cat No. 89640) staining for the assessment of proteoglycan content in the cartilage. Articular cartilage changes were evaluated by a single-blinded observer and scored using the Osteoarthritis Research Society International (OARSI) semi-quantitative system validated for use in guinea pigs. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The OARSI scoring method considers five aspects of cartilage health: structural alterations, proteoglycan content, cellularity, tidemark condition, and osteophyte formation. The pictures of stained slides were taken using the EVOS\u0026trade; FL Auto Imaging System.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.5 In-silico\u003c/em\u003e analysis for miRNA target prediction\u003c/h2\u003e \u003cp\u003eThe top dysregulated miRNAs were selected for \u003cem\u003ein-silico\u003c/em\u003e target gene prediction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to delve into their plausible role in regulating the pathogenesis of OA. Three miRNA target prediction software programs (TargetScanHuman 7.1, miRTarBase, and DIANA-microT-CDS) were used to predict the gene targets of each of the 12 top dysregulated miRNAs. TargetScanHuman 7.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.targetscan.org/vert_80/\u003c/span\u003e\u003cspan address=\"https://www.targetscan.org/vert_80/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) predicted the \u003cem\u003ede novo\u003c/em\u003e target genes, miRTarBase \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tools4mirs.org/software/mirna_databases/mirtarbase/\u003c/span\u003e\u003cspan address=\"https://tools4mirs.org/software/mirna_databases/mirtarbase/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e predicted the experimentally verified target genes, and DIANA microT-CDS \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.microrna.gr/microT-CDS\u003c/span\u003e\u003cspan address=\"http://www.microrna.gr/microT-CDS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e predicted the target genes that, in addition to binding to the 3'-UTR of the mRNA, also bind via their coding sequence to the miRNA. The common gene targets from the prediction tools were identified, providing a focused set of genes likely to be regulated by the selected miRNAs. The common target genes served as input for building a protein-protein interaction (PPI) network via STRING \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e for comprehensive interaction analysis. The hub genes in the network were analysed using the cytoHubba plug-in of Cytoscape software (version 3.9.1). CytoHubba provided an intuitive interface for the exploration and identification of critical nodes within the network that are closely associated with OA. The common gene targets of each miRNA were further analyzed for pathway enrichment using the KEGG database via ShinyGO \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.sdstate.edu/go77/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.sdstate.edu/go77/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, a web-based application designed to extract actionable insights from gene sets and elucidate the associated molecular pathways. In addition, the OsteoDIP database \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ophid.utoronto.ca/OsteoDIP/Home.faces\u003c/span\u003e\u003cspan address=\"https://ophid.utoronto.ca/OsteoDIP/Home.faces\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e was also employed to specifically filter and identify genes that are associated with OA from the list of common targets (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). To further shortlist the top target genes for each miRNA, a comparison was made between the hub genes, the genes involved in at least two OA-related pathways, and the OA-related genes identified through the OsteoDIP analysis. The genes that were common in at least two of these lists were selected as the candidate target genes for each miRNA. This strategy integrated multiple bioinformatics tools, allowing a focused identification of miRNA target genes and ensuring their relevance to OA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Cell Culture\u003c/h2\u003e \u003cp\u003eChondrosarcoma cell line SW1353 was obtained from ATCC (American Type Culture Collection), USA. Cells were cultured in Dulbecco\u0026rsquo;s Modified Eagle\u0026rsquo;s Medium (DMEM, Himedia) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Himedia, Cat. No. A002A). Cells were maintained at 37\u0026deg;C in a humidified incubator with 5% CO₂. The medium was replaced every 2\u0026ndash;3 days, and cells were passaged upon reaching 80\u0026ndash;90% confluency using trypsin-EDTA (Himedia, Cat. No. TCL070). For all experiments, cells between passages 27\u0026ndash;30 were used to ensure consistency and reproducibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Transfection and RT-qPCR\u003c/h2\u003e \u003cp\u003eSW1353 cells were seeded in 24-well plates at a density of 9 \u0026times; 10⁴ cells per well. After 24h, when cells reached 60\u0026ndash;70% confluency, the medium was replaced with antibiotic-free DMEM supplemented with 5% FBS. Cells were transfected with 50 nM miR-7-5p mimics or 100 nM miR-7-5p inhibitors (miRCURY LNA miRNA Mimic/Inhibitor, QIAGEN) using HiPerfect Transfection Reagent (Qiagen) following the manufacturer\u0026rsquo;s protocol. The HiPerfect\u0026ndash;oligonucleotide complexes were prepared in serum and antibiotic-free DMEM. Scrambled negative control oligonucleotides (Qiagen) were transfected at equivalent concentrations (50 nM and 100 nM) to account for non-sequence-specific effects. 48h post-transfection, total RNA was extracted using TRIzol\u0026reg; reagent (Invitrogen), and RNA concentration and purity were assessed using a NanoDrop\u0026reg; ND-1000 spectrophotometer (Thermo Fisher Scientific).\u003c/p\u003e \u003cp\u003eFor miRNA quantification, 200 ng of total RNA was reverse-transcribed using the miRCURY LNA RT Kit (QIAGEN), and RT-qPCR was performed using the miRCURY LNA SYBR Green PCR Kit with specific primers for hsa-miR-7-5p (Cat. No. 339306, Qiagen). U6 snRNA served as the endogenous control for normalization. For mRNA expression analysis of target genes, including \u003cem\u003eRELA(p65), RAF-1, TGFB2, SNCA, PIK3CD, IRS-1, EIF4E\u003c/em\u003e, and \u003cem\u003eARRB1\u003c/em\u003e, Reverse transcription of 1 \u0026micro;g total RNA was carried out using the iScript\u0026trade; cDNA Synthesis Kit (Bio-Rad). RT-qPCR was carried out using gene-specific primers (listed in Supplementary Data 4, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), with GAPDH as the internal control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll quantitative analyses of both \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e data were performed using a minimum of three biological replicates, with technical replicates included where applicable. The maximum number of animals permissible under our animal facility\u0026rsquo;s logistical guidelines was used. OARSI scores were analyzed using one-way ANOVA followed by Tukey\u0026rsquo;s post hoc test. For \u003cem\u003ein vitro\u003c/em\u003e experiments and RT-qPCR array data, statistical significance was determined using an unpaired t-test. Statistical analyses were conducted using GraphPad Prism version 9, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTarget genes of selected dysregulated miRNAs were predicted using TargetScanHuman, miRTarBase, and DIANA-microT-CDS. The common gene targets were analyzed to ensure biological relevance. Identified gene targets were analyzed via STRING for PPI network construction and hub gene identification using Cytoscape. Pathway enrichment analysis was performed using KEGG via ShinyGO, and OA-related genes were filtered using the OsteoDIP database. Candidate genes for each miRNA were shortlisted by selecting those that appeared in at least two of the following categories: hub genes, OA pathway-associated genes, and OA-related genes from OsteoDIP.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Osteoarthritic changes progress with advancing age in Dunkin-Hartley guinea pigs\u003c/h2\u003e \u003cp\u003eThe animals from the control (n\u0026thinsp;=\u0026thinsp;4), early OA (n\u0026thinsp;=\u0026thinsp;4), and late OA (n\u0026thinsp;=\u0026thinsp;5) groups were euthanised at 3, 7 and 12 months respectively. Following dissection, the femoral and tibial articular cartilage surfaces were assessed for age-associated degenerative changes. Macroscopic evaluation revealed a clear progression in cartilage damage across the groups. In the control animals, the articular surfaces appeared smooth, glossy, and continuous, reflecting normal cartilage morphology with no visible signs of degeneration. In contrast, the early OA group exhibited mild surface irregularities and the presence of superficial fibrillations indicative of the initial stages of cartilage deterioration. The late OA animals demonstrated pronounced structural damage, characterised by deep fibrillations, extensive surface roughening, and exposure of the underlying subchondral bone (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe histopathological evaluation of tibial and femoral cartilage from the control, early OA, and late OA groups was performed using the OARSI scoring system to assess the severity of osteoarthritic changes in the Dunkin-Hartley guinea pigs. The OARSI system quantifies cartilage damage based on the depth and extent of degeneration, with higher scores indicating more severe pathology. Each individual section was assigned a maximum possible score of 18, reflecting the extent of articular cartilage degeneration. The results revealed an age-dependent progression of osteoarthritis, as evidenced by histopathology (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and increased OARSI scores. In the control group, H\u0026amp;E-stained sections displayed a smooth and continuous articular surface, normal chondrocyte distribution, and a single, well-defined tidemark which delineates the transition between calcified and uncalcified cartilage. The early OA group exhibited mild surface irregularities and slight hypercellularity, although the tidemark remained intact. In contrast, the late OA group demonstrated pronounced degenerative features, including deep surface fissures extending into the middle zone, marked hypercellularity with prominent chondrocyte clustering. Tidemark duplication, indicative of its progression into noncalcified cartilage, was a prominent feature\u003c/p\u003e \u003cp\u003eThe proteoglycan content assessment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) revealed that in the control group, both safranin O-fast green and toluidine blue demonstrated intense and uniform staining throughout the articular cartilage, indicating abundant proteoglycan content. The early OA group showed a noticeable reduction in staining intensity, particularly in the superficial zone, reflecting depletion of glycosaminoglycans (GAGs). In the late OA group, staining intensity was markedly diminished across the superficial, middle, and deep zones, consistent with advanced proteoglycan loss and severe cartilage degeneration. The overall OARSI scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) in the control group ranged from 0 to 1, consistent with normal cartilage morphology and the absence of degenerative changes. In the early OA group, scores ranged from 3 to 5, indicative of mild but discernible structural deterioration. The late OA group exhibited OARSI scores ranging from 8 to 12, reflecting advanced cartilage degeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Statistical analysis using one-way ANOVA followed by Tukey\u0026rsquo;s post hoc test revealed significant differences between the control and early OA groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the control and late OA groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and also the early and late OA groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Collectively, these findings validate the progressive, age-associated nature of osteoarthritic changes in the Dunkin-Hartley guinea pig model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 RT-qPCR arrays revealed a downregulation of miRNAs in cartilage correlating with OA progression\u003c/h2\u003e \u003cp\u003eThe RT-qPCR array analysis of 100 miRNAs (listed in Supplementary Data 1, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) potentially associated with OA revealed a predominance of downregulation across all comparisons. Three distinct analyses were performed: early OA vs. control, late OA vs. control, and late OA vs. early OA. This comparative framework allowed the identification of miRNAs dysregulated at disease onset as well as those altered during disease progression. The RT-qPCR array profiling of the selected panel of 100 miRNAs revealed a predominance of downregulated miRNAs across all three comparisons. This trend suggests a global suppression of miRNA expression in OA cartilage, potentially reflecting widespread disruption of regulatory networks involved in cartilage homeostasis and OA pathophysiology.\u003c/p\u003e \u003cp\u003eIn the early OA vs. control group, two miRNAs, miR-101-3p (3.02-fold regulation) and miR-92b-3p (3.25-fold regulation), were significantly upregulated, while four miRNAs, miR-130b-5p (-5.11-fold regulation), miR-130b-3p (-4.71-fold regulation), miR-7-5p (-3.18-fold regulation), and miR-20b-5p (-3.66-fold regulation), were significantly downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In the late OA vs. control group, 38 miRNAs exhibited significant downregulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), with miR-130b-5p (-65.84-fold regulation), miR-10b-5p (-33.22-fold regulation), miR-379-5p (-32.76-fold regulation), miR-455-5p (-25.15-fold regulation) and miR-148b-3p (-25.06-fold regulation) among the top five downregulated miRNAs. The late OA vs. early OA comparison revealed the downregulation of 40 miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), highlighting the extent of molecular dysregulation in late OA cartilage. Among these, miR-10b-5p (-38.69-fold regulation), miR-101-3p (-34.71-fold regulation), miR-148b-3p (-33.9-fold regulation), miR-455-5p (-32.41-fold regulation) and miR-191-5p (-29.3-fold regulation) were the top 5 downregulated miRNAs. The full list of miRNAs and their respective fold changes is provided in Supplementary Data 2, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, the four miRNAs that were downregulated in the early OA group vs. the control group, miR-130b-5p, miR-130b-3p, miR-7-5p, and miR-20b-5p, showed an even more pronounced decrease in the late OA group (-8.15, -65.84, -22.09, and \u0026minus;\u0026thinsp;13.38-fold regulation, respectively, vs. control), indicating their possible role in OA progression. The consistent downregulation of these miRNAs across disease stages is further illustrated in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). These miRNAs are likely crucial regulators of OA progression and represent promising candidates for therapeutic intervention.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 OA-focused\u003c/b\u003e \u003cb\u003eIn-silico\u003c/b\u003e \u003cb\u003eanalysis for predicting miRNA target genes revealed candidate miRNAs potentially implicated in OA pathogenesis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 13 miRNAs were initially selected for \u003cem\u003ein-silico\u003c/em\u003e target prediction. This panel included the top dysregulated miRNAs, miR-130b-3p, miR-10b-5p, miR-379-5p, miR-455-5p, miR-148b-3p, miR-154-5p, miR-411a-3p, let-7g-5p, miR-369-5p, miR-376a-3p, miR-342-3p, as well as the two miRNAs miR-7-5p and miR-20b-5p, which demonstrated a progressive decline in expression with disease advancement (as detailed in the supplementary data 2, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Although miR-130b-3p, miR-379-5p, miR-455-5p, and miR-411a-3p exhibited marked dysregulation with substantial fold changes, these miRNAs were excluded from further \u003cem\u003ein-silico\u003c/em\u003e analysis due to prior literature documenting their gene targets and therapeutic roles in OA (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Thus, miRNAs with strong dysregulation but limited existing functional characterisation were chosen, thereby prioritising miRNAs with the potential to reveal novel regulatory mechanisms. The expression profiles of the selected miRNAs in the early OA vs control, late OA vs control and late OA vs early OA are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C, respectively. Further, the \u003cem\u003eIn-silico\u003c/em\u003e target gene prediction of the selected miRNAs (Supplementary data 3, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) revealed that miR-10b-5p, miR-154-5p, miR-369-5p, miR-376a-3p, and miR-342-3p demonstrated poor target prediction when analysed using an OA-focused strategy (Supplementary data 3, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These findings suggest that these miRNAs may play a less significant role in OA pathogenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, miR-148b-3p, let-7g-5p, miR-7-5p, and miR-20b-5p emerged as the most promising candidates for further investigation. miR-148b-3p and let-7g-5p were selectively downregulated in the late OA group, indicating that their dysregulation is associated with molecular changes characteristic of advanced disease stages. \u003cem\u003eIn-silico\u003c/em\u003e target prediction revealed substantial overlap across TargetScan, miRTarBase, and DIANA microT-CDS for both miRNAs. For miR-148b-3p (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), mapping of these predicted gene targets with the OsteoDIP database yielded 40 OA-associated target genes. Subsequent network and pathway enrichment analyses revealed significant clustering within OA-relevant signalling cascades, notably the FoxO, ErbB, TGF-β, Oxytocin, and PI3K-Akt pathways. Based on network connectivity and pathway relevance, \u003cem\u003eCDKN1A\u003c/em\u003e (Cyclin Dependent Kinase Inhibitor 1A), \u003cem\u003eDCP2\u003c/em\u003e (Decapping mRNA 2), \u003cem\u003eROCK1\u003c/em\u003e (Rho Associated Coiled-Coil Containing Protein Kinase 1), \u003cem\u003eRGMA\u003c/em\u003e (Repulsive Guidance Molecule A), \u003cem\u003eSOS2\u003c/em\u003e (SOS Ras/Rac Guanine Nucleotide Exchange Factor 2), \u003cem\u003eITGB8\u003c/em\u003e (Integrin Subunit Beta 8), \u003cem\u003eNRAS\u003c/em\u003e (Neuroblastoma RAS Viral Oncogene Homolog), and \u003cem\u003eTXNIP\u003c/em\u003e (Thioredoxin Interacting Protein) were shortlisted as putative OA-relevant targets of miR-148b-3p. A similar integrative approach for let-7g-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB) identified 53 OA-associated targets through OsteoDIP mapping, with protein-protein interaction analysis indicating strong network connectivity. Pathway enrichment further underscored prominent associations with the FoxO and PI3K-Akt signalling pathways, suggesting shared regulatory roles of both the miRNAs in OA pathophysiology. Consequently, \u003cem\u003eCASP3\u003c/em\u003e (Caspase 3), \u003cem\u003eCCND1\u003c/em\u003e (Cyclin D1), \u003cem\u003eCDKN1A\u003c/em\u003e (Cyclin Dependent Kinase Inhibitor 1A), \u003cem\u003eBCL2L1\u003c/em\u003e (BCL2 Like 1), \u003cem\u003eCDC25A\u003c/em\u003e (Cell Division Cycle 25A), \u003cem\u003eMDM4\u003c/em\u003e (MDM4 Regulator of P53), \u003cem\u003eIGF1R\u003c/em\u003e (Insulin Like Growth Factor 1 Receptor), \u003cem\u003eTHBS1\u003c/em\u003e (Thrombospondin 1), and \u003cem\u003eTGFBR1\u003c/em\u003e (Transforming Growth Factor Beta Receptor 1) were shortlisted as key target genes of let-7g-5p.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003emiR-7-5p and miR-20b-5p exhibited a progressive downregulation in expression from early to late OA, suggesting their potential involvement in OA progression. Consistent with its progressive downregulation during disease progression, miR-7-5p exhibited substantial target overlap across prediction platforms, with 66 OA-associated genes identified using the OsteoDIP database. Network analysis revealed multiple OA-relevant hub genes, and pathway enrichment analysis indicated significant involvement of the HIF-1, FoxO, Insulin signalling, and Cellular senescence pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Accordingly, \u003cem\u003eRAF1\u003c/em\u003e (RAF Proto-Oncogene Serine/Threonine Kinase), \u003cem\u003eRELA(p65)\u003c/em\u003e (RELA Proto-Oncogene, NF-κB Subunit), \u003cem\u003eIRS1\u003c/em\u003e (Insulin Receptor Substrate 1), \u003cem\u003ePIK3CD\u003c/em\u003e (Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Delta), \u003cem\u003eTGFB2\u003c/em\u003e (Transforming Growth Factor Beta 2), \u003cem\u003eEIF4E\u003c/em\u003e (Eukaryotic Translation Initiation Factor 4E), \u003cem\u003eSNCA\u003c/em\u003e (Synuclein Alpha), \u003cem\u003eARRB1\u003c/em\u003e (Arrestin Beta 1), and \u003cem\u003ePIK3R3\u003c/em\u003e (Phosphoinositide-3-Kinase Regulatory Subunit 3) were shortlisted as the most relevant targets of miR-7-5p. Likewise, miR-20b-5p demonstrated extensive target overlap, with 33 OA-associated genes identified via the OsteoDIP database. Network and pathway analyses revealed strong connectivity and enrichment of pathways related to hypoxia signalling and the ErbB signalling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The final shortlisted targets included \u003cem\u003eHIF1A\u003c/em\u003e (Hypoxia Inducible Factor 1 Subunit Alpha), \u003cem\u003ePTEN\u003c/em\u003e (Phosphatase and Tensin Homolog), \u003cem\u003eSTAT3\u003c/em\u003e (Signal Transducer and Activator of Transcription 3), \u003cem\u003eCDKN1A\u003c/em\u003e (Cyclin Dependent Kinase Inhibitor 1A), \u003cem\u003eBCL2L11\u003c/em\u003e (BCL2 Like 11), \u003cem\u003eHSPA8\u003c/em\u003e (Heat Shock Protein Family A Member 8), and \u003cem\u003eSQSTM1\u003c/em\u003e (Sequestosome 1).\u003c/p\u003e \u003cp\u003eCollectively, these \u003cem\u003ein-silico\u003c/em\u003e analyses demonstrate that miR-148b-3p and let-7g-5p are likely involved in molecular mechanisms underlying late-stage OA, whereas miR-7-5p and miR-20b-5p appear to regulate pathways contributing to OA progression. The extensive target overlap, identification of biologically relevant hub genes, and enrichment of OA-associated pathways strongly support the prioritization of these miRNAs for subsequent functional validation. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises the results of the \u003cem\u003ein-silico\u003c/em\u003e analysis and highlights the final shortlisted gene targets for all selected miRNAs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eIn-silico\u003c/em\u003e target gene prediction results for selected miRNAs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003emiRNA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTotal Predicted Gene Targets\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTop Hub Genes\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eOA Associated Pathways\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eFinal Shortlisted Gene targets\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon Gene Targets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOA Related Genes (OsteoDip)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-10b-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGATA3, NCOR2, KLF4, ZMYND11, NCOA6, PPARA, TFAP2C, BCL2L11, SRSF1, TRA2B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNCOR2, KLF4, BCL2L11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-148b-3p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAGO1, TNRC6A, CDKN1B, DCP2, DNMT1, PRKAA1, PATL1, PRNP, TNRC6B, KPNA4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFoxO, ErbB, TGF-beta, Oxytocin, PI3K-Akt signalling pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCDKN1B, DCP2, ROCK1, RGMA, SOS2, ITGB8, NRAS, TXNIP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-154-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePPP1CC, PCGF5, NRBF2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elet-7g-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTHBS1, CASP3, IGF1R, CCND1, E2F6, CDC25A, MDM4, BCL2L1, CDKN1A, TGFBR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelaxin, FoxO,\u003c/p\u003e \u003cp\u003ePI3K-Akt signalling pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCASP3, CCND1, CDKN1A, BCL2L1, CDC25A, MDM4, IGF1R, THBS1, TGFBR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-369-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-7-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eARRB1, IRS1, PIK3CD, EGFR, EIF4E, RELA, RAF1, SNCA, TPGS2, CHAMP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInsulin resistance, HIF-1, Relaxin, FoxO, Cellular senescence signalling pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eRAF1, RELA, IRS1, PIK3CD, TGFB2, EIF4E, SNCA, ARRB1, PIK3R3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-376a-3p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-342-3p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-20b-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHIF-1A, PTEN, SMAD5, STAT3, BCL2L11, CDKN1A, HSPA8, DDX5, SQSTM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHIF-1, ErbB signalling pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eHIF-1A, PTEN, STAT3, CDKN1A, BCL2L11, HSPA8, SQSTM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eIn-silico\u003c/em\u003e target gene prediction results for selected miRNAs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003emiRNA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTotal Predicted Gene Targets\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTop Hub Genes\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eOA Associated Pathways\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eFinal Shortlisted Gene targets\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon Gene Targets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOA Related Genes (OsteoDip)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-10b-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGATA3, NCOR2, KLF4, ZMYND11, NCOA6, PPARA, TFAP2C, BCL2L11, SRSF1, TRA2B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNCOR2, KLF4, BCL2L11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-148b-3p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAGO1, TNRC6A, CDKN1B, DCP2, DNMT1, PRKAA1, PATL1, PRNP, TNRC6B, KPNA4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFoxO, ErbB, TGF-beta, Oxytocin, PI3K-Akt signalling pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCDKN1B, DCP2, ROCK1, RGMA, SOS2, ITGB8, NRAS, TXNIP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-154-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePPP1CC, PCGF5, NRBF2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elet-7g-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTHBS1, CASP3, IGF1R, CCND1, E2F6, CDC25A, MDM4, BCL2L1, CDKN1A, TGFBR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelaxin, FoxO,\u003c/p\u003e \u003cp\u003ePI3K-Akt signalling pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCASP3, CCND1, CDKN1A, BCL2L1, CDC25A, MDM4, IGF1R, THBS1, TGFBR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-369-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-7-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eARRB1, IRS1, PIK3CD, EGFR, EIF4E, RELA, RAF1, SNCA, TPGS2, CHAMP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInsulin resistance, HIF-1, Relaxin, FoxO, Cellular senescence signalling pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eRAF1, RELA, IRS1, PIK3CD, TGFB2, EIF4E, SNCA, ARRB1, PIK3R3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-376a-3p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-342-3p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiR-20b-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHIF-1A, PTEN, SMAD5, STAT3, BCL2L11, CDKN1A, HSPA8, DDX5, SQSTM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHIF-1, ErbB signalling pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eHIF-1A, PTEN, STAT3, CDKN1A, BCL2L11, HSPA8, SQSTM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eTotal predicted gene targets, key hub genes from network analysis, OA-related pathways, and shortlisted gene targets identified using an OA-focused strategy. \"NA\" denotes the absence of significant results from hub gene analysis or Gene Set Enrichment Analysis. Full gene names for all genes are provided in Supplementary Data 3\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTotal predicted gene targets, key hub genes from network analysis, OA-related pathways, and shortlisted gene targets identified using an OA-focused strategy. \"NA\" denotes the absence of significant results from hub gene analysis or Gene Set Enrichment Analysis. Full gene names for all genes are provided in Supplementary Data 3\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4\u003c/b\u003e \u003cem\u003eI\u003c/em\u003e\u003cb\u003en-vitro\u003c/b\u003e \u003cb\u003evalidation of miR-7-5p gene targets\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo prioritise candidates for further investigation, we focused on miRNAs associated with disease progression. Among them, miR-7-5p showed a markedly higher degree of downregulation (-22.09-fold) compared to miR-20b-5p (-13.38-fold), prompting functional validation of its predicted gene targets. We performed gain and loss-of-function experiments in SW1353 cells. The cells were transfected with miR-7-5p mimics (50 nM) and inhibitors (100 nM) to evaluate the effect of miR-7-5p modulation on the mRNA expression of its candidate target genes: \u003cem\u003eRELA (p65), RAF-1, TGFB2, SNCA, PIK3CD, IRS-1, EIF4E\u003c/em\u003e, and \u003cem\u003eARRB1\u003c/em\u003e. Following transfection with the miR-7-5p mimic (50 nM), RT-qPCR analysis confirmed a significant increase in miR-7-5p expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA) compared to both the non-transfected cells (739.9 fold upregulation, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the scrambled control (357.2 fold upregulation, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Given the well-established inverse relationship between miRNAs and their target genes, overexpression of miR-7-5p resulted in a significant downregulation of its targets \u003cem\u003eRELA (p65)\u003c/em\u003e (5 fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB), \u003cem\u003eRAF-1\u003c/em\u003e (3.3 fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC), \u003cem\u003ePIK3CD\u003c/em\u003e (2.9 fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD ), \u003cem\u003eSNCA\u003c/em\u003e (4 fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE), \u003cem\u003eEIF4E\u003c/em\u003e (2.2 fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF), \u003cem\u003eIRS-1\u003c/em\u003e (2.2 fold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eG), and \u003cem\u003eARRB1\u003c/em\u003e (3.8 fold downregulation, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eH) relative to the scrambled control. These results support the regulatory influence of miR-7-5p on multiple OA-associated genes, highlighting their potential involvement in disease-related molecular pathways.\u003c/p\u003e \u003cp\u003eAlthough the transfection with the miR-7-5p inhibitor resulted in a marked suppression of miR-7-5p levels relative to the scrambled control (5-fold downregulation, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 at 50 nM and 250-fold downregulation, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 at 100 nM), it did not lead to a significant increase in the expression levels of these gene targets (Supplementary Data 4 Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), possibly due to low basal expression of endogenous miR-7-5p in SW1353 cells which restrict the extent to which target gene upregulation can be detected.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides the first comprehensive overview of miRNA expression patterns associated with OA progression in the Dunkin-Hartley guinea pig, an established model of naturally occurring OA (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The onset and progression of OA in guinea pigs are more consistent and reproducible with advancing age, reducing variability commonly associated with surgically induced models. Previous studies have shown that this model closely mimics human primary OA, displaying comparable disease progression and histopathological features (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Consistent with these reports, histopathological evaluation and the semi-quantitative OARSI scoring of knee joint sections in our study also confirmed a progressive increase in OA severity with advancing age. Moreover, the OARSI histological scores of the Dunkin-Hartley guinea pig knee joint cartilage aligned well with the OARSI grading systems used for human OA cartilage (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Control animals exhibited scores comparable to OARSI grade 0\u0026ndash;1 in humans, indicating normal or minimally altered cartilage. Early OA animals displayed scores corresponding to mild OA (human OARSI grades 2\u0026ndash;3), characterised by superficial fibrillation. Late OA animals demonstrated scores consistent with moderate to advanced OA (human OARSI grades 4\u0026ndash;5), reflecting deep fissuring, extensive matrix loss, and tidemark duplication. These histopathological findings confirmed that the Dunkin-Hartley guinea pig model reproduces the structural features of human idiopathic OA in a stage-dependent manner.\u003c/p\u003e \u003cp\u003emiRNAs function as post-transcriptional regulators of gene expression by targeting the 3\u0026prime; untranslated regions of mRNAs, leading to either their degradation or translational repression. They play a critical role in maintaining articular cartilage homeostasis, and their dysregulation has been implicated in cartilage degradation, inflammation, and aging (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In our present study, we profiled the expression of 100 selected miRNAs in the cartilage of Dunkin-Hartley guinea pigs at different stages of OA progression using RT-qPCR arrays. To enable miRNA profiling from micro-quantities of guinea pig cartilage tissue, the RNA isolation protocol was optimised for downstream RT-qPCR applications as reported previously (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). While RNA purity and concentration were satisfactory, the RIN values were suboptimal for application in high-throughput transcriptomic platforms. Such reduced RIN values are commonly observed in cartilage due to its dense ECM, low cellularity, and high RNase activity (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Despite these limitations, the RNA was of adequate quality for RT-qPCR-based quantification of miRNAs, which is less sensitive to RNA fragmentation, thereby providing a cost-effective and practical strategy for targeted miRNA profiling under conditions of limited tissue availability. These miRNAs were shortlisted based on their potential involvement in OA pathophysiology, as suggested by prior literature and predicted expression in bone. We identified a total of 37 miRNAs to be significantly downregulated in the late OA group, with miR-130b-3p, miR-10b-5p, miR-379-5p, miR-455-5p, and miR-148b-3p as the top five most downregulated miRNAs. Notably, several of these downregulated miRNAs have previously been implicated in cartilage regeneration, chondrocyte proliferation, and OA attenuation. For instance, miR-130b-3p and miR-455 have been identified in the cargo of extracellular vesicles (EVs) isolated from synovial mesenchymal stem cells (SMSCs) and bone marrow mesenchymal stem cells (BMSCs), respectively. When administered intra-articularly in surgically induced OA models, these EVs significantly alleviated disease severity by modulating the AKT/β-catenin pathway in the case of miR-130b-3p and the FOXO pathway in the case of miR-455. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In another study, miR-130b-3p was reported to be the key upregulated miRNA in the EVs isolated from the synovial fluid of OA patients, suggesting its role in cell-to-cell communication in OA cells (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Moreover, miR-130b-3p has also been reported to be involved in chondrogenic differentiation, further supporting its regulatory role in cartilage biology (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In line with our findings, Zhang et al. recently reported the downregulation of miR-455-5p in human OA cartilage and IL-1β-treated chondrocytes (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), while Min et al. had earlier shown reduced levels of miR-455-5p during cartilage development in bone matrix gelatin (BMG) rat model (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Moreover, intra-articular injection of miR-455-5p mimics in a surgically induced OA model significantly inhibited cartilage destruction (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the observed downregulation of miR-379-5p in our study aligns with prior findings in IL-1β-treated primary chondrocytes alongside human OA cartilage, where its expression was also reduced (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). \u003cem\u003eIn vivo\u003c/em\u003e administration of miR-379-5p agomir has been shown to enhance cartilage repair, further supporting its chondroprotective role. Contrary to this, the roles of top-downregulated miRs in our study, miR-10b-5p and miR-148b-3p, remain largely unexplored in OA. miR-148b-3p has been documented as a tumor suppressor in osteosarcoma (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) and a promoter of osteogenesis (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), while circulating miR-10b-5p levels have been linked to osteoporosis, indicating its involvement in bone metabolism (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Both the miRNAs were also associated with rheumatoid arthritis (RA) pathogenesis (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Together, these studies suggest shared regulatory roles for these miRNAs across joint diseases, making their further exploration in OA both relevant and compelling.\u003c/p\u003e \u003cp\u003eFurthermore, miR-130b-3p, miR-7-5p, miR-20b-5p, and miR-130b-5p were notably downregulated in both early and late OA groups compared to controls, suggesting progressive suppression of regulatory miRNAs as OA advances. miR-7-5p, which is likely to be involved in OA progression, has also been linked to osteosarcoma, osteoporosis (\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), and proposed as a diagnostic biomarker for RA (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). While we found miR-7-5p downregulated in cartilage during spontaneous OA progression, prior studies have reported context-dependent roles for miR-7-5p in OA pathogenesis. Previous \u003cem\u003ein vitro\u003c/em\u003e studies have reported upregulated miR-7-5p expression under inflammatory OA conditions, largely attributed to reduced sequestration by regulatory non-coding RNAs, including the circular RNA ciRS-7 and the lncRNA ZFAS1 (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Another study reported increased miR-7-5p expression in a rat OA model, where elevated miR-7-5p impaired chondrocyte autophagy by targeting ATG4A, contributing to cartilage degeneration (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Notably, a previous study reported reduced expression of miR-7-5p in OA cartilage and demonstrated that overexpression of miR-7-5p in ATDC5 cells enhanced cartilage proteoglycan content while preventing chondrocyte hypertrophy (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Another study has reported its downregulation in the serum of OA patients (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). These discrepancies likely reflect tissue and model-specific dynamics and also the presence of competing non-coding RNAs. Our findings may capture early intrinsic changes unique to spontaneous OA. Thus, miR-7-5p remains a biologically relevant candidate for further investigation.\u003c/p\u003e \u003cp\u003eIn addition, previous studies have also highlighted miR-20b-5p as a regulatory miRNA with potential protective roles in OA pathogenesis. Through integrative bioinformatic analyses, miR-20b-5p was identified as a significantly dysregulated miRNA within OA-associated regulatory networks and a key node capable of modulating multiple OA-relevant targets (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Experimental evidence further demonstrated that miR-20b-5p regulates angiogenesis in cartilage and subchondral bone in an early-stage OA rat model, with its dysregulation promoting aberrant vascular invasion and subchondral bone remodelling, thereby contributing to cartilage degeneration (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe consistent downregulation of miRNAs observed in aging cartilage in our study likely reflects a progressive loss of cartilage homeostasis, driven primarily by suppression of miRNA expression rather than selective biogenesis of miRNAs. Advancing OA is associated with chondrocyte senescence and apoptosis, which can impair miRNA biogenesis and contribute to widespread miRNA downregulation. Furthermore, as primary OA is an age-related disease, aging cartilage also exhibits epigenetic alterations, including DNA methylation of miRNA promoters and histone modifications, leading to transcriptional silencing of miRNAs (\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). These effects may preferentially impact miRNAs involved in cartilage homeostasis and tissue maintenance, thereby explaining the predominance of downregulated miRNAs with disease progression. This may contribute to enhanced expression of catabolic and inflammatory genes.\u003c/p\u003e \u003cp\u003eTo strengthen the biological relevance of our miRNA profiling results and to identify candidate miRNAs with potential therapeutic value, miRNAs with strong dysregulation but limited existing functional characterization were chosen, thereby prioritizing miRNAs with the potential to reveal novel regulatory mechanisms.\u003c/p\u003e \u003cp\u003eWe conducted an OA-focused \u003cem\u003ein silico\u003c/em\u003e analysis on the top-selected miRNAs (miR-10b-5p, miR-148b-3p, miR-154-5p, let-7g-5p, miR-369-5p, miR-7-5p, miR-376a-3p, miR-342-3p, and miR-20b-5p). For this, OsteoDIP database (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), a curated platform specific to osteoarthritis research, was used to improve the specificity of OA-related target prediction. Additionally, gene set enrichment analysis (GSEA) was employed to identify perturbed molecular functions and pathways, while hub gene network analysis provided an overview of the genes most closely associated with the disease. Such integrated \u003cem\u003ein silico\u003c/em\u003e approaches have previously proven valuable in OA research, offering a bird\u0026rsquo;s-eye view of transcriptional regulation and helping prioritize miRNAs with functional and therapeutic relevance (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Interestingly, although miR-10b-5p was among the most downregulated miRNAs and was previously implicated in joint diseases, its predicted gene targets and associated OA-relevant pathways did not show strong support for a direct role in OA pathogenesis. Similarly, miR-154-5p, miR-376a-3p, miR-342-3p, and miR-369-5p also lacked compelling pathway associations and subsequent hub genes in this context.\u003c/p\u003e \u003cp\u003eBased on the present \u003cem\u003ein-silico\u003c/em\u003e results, miR-148b-3p, let-7g-5p, miR-7-5p, and miR-20b-5p demonstrated better prospects among all identified miRNA-mRNA regulatory networks and pathways. Among late OA-specific miRNAs, miR-148b-3p and let-7g-5p were predicted to be involved in the FOXO and PI3K-Akt signalling pathways, both of which are known to influence chondrocyte survival and cartilage matrix homeostasis (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). miR-7-5p and miR-20b-5p, which were found to be involved in OA progression, were predicted to regulate the HIF-1 signaling pathway, a key contributor to OA pathology (\u003cspan additionalcitationids=\"CR59 CR60\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Additionally, miR-7-5p was associated with several well-established OA-related pathways, including insulin signaling, FOXO signaling, relaxin signaling, and cellular senescence regulation, supporting its potential role in cartilage degeneration and joint aging (\u003cspan additionalcitationids=\"CR63 CR64 CR65 CR66 CR67\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Together, our \u003cem\u003ein silico\u003c/em\u003e analysis highlights the miRNAs with plausible roles in OA pathogenesis and supports their prioritization for functional validation. However, further experimental studies are needed to elucidate their precise biological mechanisms.\u003c/p\u003e \u003cp\u003eGiven the potential of miRNAs to reveal early molecular events underlying idiopathic OA, where triggers are still poorly defined, their identification is crucial for developing targeted, miRNA-based therapies. In our study, miR-7-5p emerged as a key miRNA associated with OA progression with a greater fold change than miR-20-5p. Furthermore, its predicted association with multiple OA pathways and established involvement in inflammatory processes (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e), miR-7-5p emerged as a compelling candidate for \u003cem\u003ein vitro\u003c/em\u003e investigation. The upregulation of miR-7-5p in SW1353 cells led to the downregulation of its target genes \u003cem\u003eRELA (p65), RAF-1, TGFB2, SNCA, PIK3CD, IRS-1, EIF4E, and ARRB1\u003c/em\u003e, highlighting the regulatory role of miR-7-5p. \u003cem\u003eRELA (p65)\u003c/em\u003e, a central NF-κB subunit, drives OA pathogenesis by promoting catabolic enzymes like ADAMTS5 and MMPs (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Knockout and inhibition studies \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e show that silencing \u003cem\u003eRELA\u003c/em\u003e reduces cartilage damage and chondrocyte apoptosis (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e) (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). \u003cem\u003eRAF-1\u003c/em\u003e plays a key role in mediating inflammatory responses in OA (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Additionally, its co-activation with \u003cem\u003eRELA (p65)\u003c/em\u003e has been shown to enhance MMP-3 production in osteoarthritic synovial fibroblasts (OASFs) via the MAPK/ERK pathway (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). Contrary to this, TGFB2 plays a context-dependent role in OA and regulates early chondrogenesis (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). Although it is chondroprotective at physiological levels, excessive TGF-β signalling may contribute to synovial fibrosis and osteophyte formation. Thus, maintaining balanced, tissue-specific activation is critical (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). \u003cem\u003eSNCA\u003c/em\u003e, primarily studied in neurodegenerative disorders (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e), showed downregulation in our study, hinting at a possible OA-neurodegeneration link. This can be explored further, given the reported co-occurrence of OA and neurodegenerative conditions in aging populations (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). The gene target IGF-1 is a key factor for the maintenance of bone homeostasis (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e), and modulations in the IGF-1/AKT/IRS-1 signalling axis have been associated with OA pathology (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). Although \u003cem\u003ePIK3CD, EIF4E\u003c/em\u003e, and \u003cem\u003eARRB1\u003c/em\u003e remain less characterized in OA (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e), their known roles in PI3K/AKT signalling, translational control, and GPCR desensitization suggest potential relevance and merit further functional investigation.\u003c/p\u003e \u003cp\u003eSeveral validated targets, \u003cem\u003eRELA, RAF-1, SNCA, TGFB2\u003c/em\u003e, and \u003cem\u003eIRS-1\u003c/em\u003e, have been previously confirmed as miR-7-5p targets via luciferase and RNA immunoprecipitation (RIP) assays in other diseases, reinforcing our findings (\u003cspan additionalcitationids=\"CR87 CR88 CR89 CR90 CR91 CR92 CR93 CR94\" citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e). This strengthens the robustness of the bioinformatic strategy employed for gene target prediction. In our study, miR-7-5p inhibition did not markedly alter target gene levels, likely owing to its low expression in SW1353 cells. The observation suggests that these cells are better suited for miR-7-5p gain-of-function studies. More robust knockdown strategies, like CRISPR-Cas or shRNA may yield clearer outcomes in future investigations.\u003c/p\u003e \u003cp\u003eOur findings highlight the utility of RT-qPCR array-based profiling as a sensitive tool for identifying miRNA alterations, even in microquantities of cartilage tissue. This study offers a comprehensive overview of miRNA dysregulation in idiopathic OA and reinforces the translational relevance of the Dunkin-Hartley guinea pig model for investigating OA pathogenesis and evaluating pharmacological and non-pharmacological interventions. The identified downregulated miRNAs emerge as promising candidates for therapeutic exploration and for deciphering the epigenetic regulation of OA. Additionally, exploring these downregulated miRNAs as a therapeutic panel may offer translational potential. However, in contrast to their well-established role as diagnostic biomarkers, miRNA-based combination therapies remain largely conceptual and face substantial challenges that must be addressed before clinical application. The present study has certain limitations that should be acknowledged. Although the use of SW1353 cells, a chondrosarcoma cell line, provides a convenient experimental system, it has limited ability to fully mimic the OA chondrocyte phenotype. Furthermore, the lack of validation of candidate miRNAs in human OA cartilage samples is a limitation, and future studies involving human tissues will be necessary to enhance translational relevance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eAll authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eNidhi Bhardwaj: Writing- original draft, validation, methodology, investigation, formal analysis, data curation, conceptualization. Diksha Rana: Methodology, investigation, supervision, and formal analysis. Alka Bhatia: supervision, resources, validation and formal analysis. Uttam Chand Saini: supervision and resources. Jyotdeep Kaur: project administration, supervision, research funding, methodology, conceptualization, writing- review and editing.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Approval\u003c/h2\u003e \u003cp\u003e All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. The research reported in this article was performed with approval of the Institutional Animal Ethics Committee (108/100/99/IAEC/688) of Postgraduate Institute of Medical Education and Research (PGIMER).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical Trial Number\u003c/strong\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eWe gratefully acknowledge the \u003cb\u003eIndian Council of Medical Research (ICMR)\u003c/b\u003e for funding the study (Grant No. 5/4\u0026ndash;5/3/1/Ortho/2019/NCD-1), which supported the experimental work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNidhi Bhardwaj: Writing- original draft, validation, methodology, investigation, formal analysis, data curation, conceptualization. Diksha Rana: Methodology, investigation, supervision, and formal analysis. Alka Bhatia: supervision, resources, validation and formal analysis. Uttam Chand Saini: supervision and resources. Jyotdeep Kaur: project administration, supervision, research funding, methodology, conceptualization, writing- review and editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset generated during this study is uploaded as supplementary data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLong H, Liu Q, Yin H, Wang K, Diao N, Zhang Y, et al. Prevalence Trends of Site-Specific Osteoarthritis From 1990 to 2019: Findings From the Global Burden of Disease Study 2019. Arthritis Rheumatol. 2022;74(7):1172\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/art.42089\u003c/span\u003e\u003cspan address=\"10.1002/art.42089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen KD, Thoma LM, Golightly YM. 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Microrna. 2022;11(1):57\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/2211536611666220413100636\u003c/span\u003e\u003cspan address=\"10.2174/2211536611666220413100636\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cartilage, Dunkin-Hartley guinea pig, miRNA, osteoarthritis, idiopathic OA","lastPublishedDoi":"10.21203/rs.3.rs-8914957/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8914957/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOsteoarthritis (OA) is the leading cause of joint disability in the elderly, particularly affecting the knee. The molecular mechanisms underlying idiopathic, age-related OA remain unclear, and current therapies are largely symptom-targeted. MicroRNAs (miRNAs) are key regulators of cartilage homeostasis and inflammation, but most \u003cem\u003ein vivo\u003c/em\u003e models fail to recapitulate idiopathic, age-associated OA.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe Dunkin-Hartley guinea pig model of spontaneous OA was used to investigate aging-related miRNA dysregulation. Cartilage RNA was extracted from control, early OA, and late OA groups and profiled for 100 miRNAs using RT-qPCR arrays. Histological evaluation and OARSI scoring assessed cartilage degeneration. Dysregulated miRNAs were analyzed using a bioinformatic pipeline incorporating target prediction (TargetScan, miRTarBase, DIANA microT-CDS), protein-protein interaction (STRING), network analysis (Cytoscape), and pathway enrichment (KEGG). Functional validation was performed by transfecting SW1353 cells with miR-7-5p mimics and assessing expression of predicted target genes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHistological analysis and OARSI scoring confirmed progressive cartilage degeneration. RT-qPCR arrays revealed significant age-dependent downregulation of miR-130b-3p, miR-7-5p, miR-20b-5p, and miR-130b-5p, with 38 miRNAs downregulated in late OA relative to controls. Network and pathway analysis identified miR-7-5p as a central regulator of OA pathogenesis. Transfection of SW1353 cells with miR-7-5p mimics suppressed expression of predicted gene targets, including \u003cem\u003eRELA(p65), RAF-1, TGFB2, IRS1, SNCA, PIK3CD, EIF4E\u003c/em\u003e, and \u003cem\u003eARRB1\u003c/em\u003e, confirming an inverse regulatory relationship.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study delineates aging-related dysregulated miRNAs in idiopathic OA and highlights miR-7-5p as a key regulator of disease pathogenesis. The study highlights the Dunkin-Hartley guinea pig as a valuable translational model for exploring primary OA pathogenesis and advancing miRNA-based therapies in OA.\u003c/p\u003e","manuscriptTitle":"Comprehensive microRNA profiling of aging cartilage in the Dunkin-Hartley guinea pig model identifies key regulators of osteoarthritis progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 19:48:16","doi":"10.21203/rs.3.rs-8914957/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"65096099355174070969854117211353158026","date":"2026-05-19T00:23:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124865048539103892753968755391526920209","date":"2026-05-18T08:23:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81333192442785169872182171570248565804","date":"2026-05-18T04:32:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T13:50:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335395012659516830135068892477661523542","date":"2026-04-15T22:13:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308032551374030028378681691709780792634","date":"2026-04-03T05:04:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164173130961013982398145409091577858040","date":"2026-03-22T07:00:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T19:00:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-20T12:33:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-20T12:32:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2026-02-19T07:14:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb621b6a-4e95-442d-b1a4-9d0adb853afd","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"65096099355174070969854117211353158026","date":"2026-05-19T00:23:34+00:00","index":67,"fulltext":""},{"type":"reviewerAgreed","content":"124865048539103892753968755391526920209","date":"2026-05-18T08:23:13+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"81333192442785169872182171570248565804","date":"2026-05-18T04:32:48+00:00","index":65,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T13:50:38+00:00","index":52,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T19:48:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 19:48:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8914957","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8914957","identity":"rs-8914957","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}