Murine cartilage microbial DNA deposition occurs rapidly following the introduction of a gut microbiome and changes with obesity, aging, and knee osteoarthritis.

Cartilage microbial DNA patterns have been recently characterized in osteoarthritis (OA). The objectives of this study were to evaluate the gut origins of cartilage microbial DNA, to characterize cartilage microbial changes with age, obesity, and OA in mice, and correlate these to gut microbiome changes. We used 16S rRNA sequencing performed longitudinally on articular knee cartilage from germ-free (GF) mice following oral microbiome inoculation and cartilage and cecal samples from young and old wild-type mice with/without high-fat diet-induced obesity (HFD) and with/without OA induced by destabilization of the medial meniscus (DMM) to evaluate gut and cartilage microbiota. Microbial diversity was assessed, groups compared, and functional metagenomic profiles reconstructed. Findings were confirmed in an independent cohort by clade-specific qPCR. We found that cartilage microbial patterns developed at 48 h and later timepoints following oral microbiome inoculation of GF mice. Alpha diversity was increased in SPF mouse cartilage samples with age (P = 0.013), HFD (P = 5.6E-4), and OA (P = 0.029) but decreased in cecal samples with age (P = 0.014) and HFD (P = 1.5E-9). Numerous clades were altered with aging, HFD, and OA, including increases in Verrucomicrobia in both cartilage and cecal samples. Functional analysis suggested changes in dihydroorotase, glutamate-5-semialdehyde dehydrogenase, glutamate-5-kinase, and phosphoribosylamine-glycine ligase, in both cecum and cartilage, with aging, HFD, and OA. In conclusion, cartilage microbial DNA patterns develop rapidly after the introduction of a gut microbiome and change in concert with the gut microbiome during aging, HFD, and OA in mice. DMM-induced OA causes shifts in both cartilage and cecal microbiome patterns independent of other factors.

Peripheral Blood DNA Methylation-Based Machine Learning Models for Prediction of Knee Osteoarthritis Progression: Biologic Specimens and Data From the Osteoarthritis Initiative and Johnston County Osteoarthritis Project.

OBJECTIVE: The lack of accurate biomarkers to predict knee osteoarthritis (OA) progression is a key unmet need in OA clinical research. The objective of this study was to develop baseline peripheral blood epigenetic biomarker models to predict knee OA progression. METHODS: Genome-wide buffy coat DNA methylation patterns from 554 individuals from the Osteoarthritis Biomarkers Consortium (OABC) were determined using Illumina Infinium MethylationEPIC 850K arrays. Data were divided into model development and validation sets, and machine learning models were trained to classify future OA progression by knee pain, radiographic imaging, knee pain plus radiographic imaging, and any progression (pain, radiographic, or both). Parsimonious models using the top 13 CpG sites most frequently selected during development were tested on independent samples from participants in the Johnston County Osteoarthritis (JoCo OA) Project (n = 128) and a previously published Osteoarthritis Initiative (OAI) data set (n = 55). RESULTS: Full models accurately classified future radiographic-only progression (mean ± SEM accuracy 87 ± 0.8%, area under the curve [AUC] 0.94 ± 0.004), pain-only progression (accuracy 89 ± 0.9%, AUC 0.97 ± 0.004), pain plus radiographic progression (accuracy 72 ± 0.7%, AUC 0.79 ± 0.006), and any progression (accuracy 78 ± 0.4%, AUC 0.86 ± 0.004). Pain-only and radiographic-only progressors were not distinguishable (mean ± SEM accuracy 58 ± 1%, AUC 0.62 ± 0.001). Parsimonious models showed similar performance and accurately classified future radiographic progressors in the OABC cohort and in both validation cohorts (mean ± SEM accuracy 80 ± 0.3%, AUC 0.88 ± 0.003 [using JoCo OA Project data], accuracy 80 ± 0.8%, AUC 0.89 ± 0.002 [using previous OAI data]). CONCLUSION: Our data suggest that pain and structural progression share similar early systemic immune epigenotypes. Further studies should focus on evaluating the pathophysiologic consequences of differential DNA methylation and peripheral blood cell epigenotypes in individuals with knee OA.

A Pilot Analysis of Genome-Wide DNA Methylation Patterns in Mouse Cartilage Reveals Overlapping Epigenetic Signatures of Aging and Osteoarthritis.

OBJECTIVE: Cartilage epigenetic changes are strongly associated with human osteoarthritis (OA). However, the influence of individual environmental OA risk factors on these epigenetic patterns has not been determined; herein we characterize cartilage DNA methylation patterns associated with aging and OA in a mouse model. METHODS: Murine knee cartilage DNA was extracted from healthy young (16-week, n = 6), old (82-week, n = 6), and young 4-week post-destabilization of the medial meniscus (DMM) OA (n = 6) C57BL6/J mice. Genome-wide DNA methylation patterns were determined via Illumina BeadChip. Gene set enrichment analysis was performed by Ingenuity Pathway Analysis. The top seven most differentially methylated positions (DMPs) were confirmed by pyrosequencing in an independent animal set. Results were compared to previously published human OA methylation data. RESULTS: Aging was associated with 20,940 DMPs, whereas OA was associated with 761 DMPs. Merging these two conditions revealed 279 shared DMPs. All demonstrated similar directionality and magnitude of change (Δß 1.0% ± 0.2%, mean methylation change ± SEM). Shared DMPs were enriched in OA-associated pathways, including RhoA signaling (P = 1.57 × 10-4 ), protein kinase A signaling (P = 3.38 × 10-4 ), and NFAT signaling (P = 6.14 × 10-4 ). Upstream regulators, including TET3 (P = 6.15 × 10-4 ), immunoglobulin (P = 6.14 × 10-4 ), and TLR7 (P = 7.53 × 10-4 ), were also enriched. Pyrosequencing confirmed six of the seven top DMPs in an independent cohort. CONCLUSION: Aging and early OA following DMM surgery induce similar DNA methylation changes within a murine OA model, suggesting that aging may induce pro-OA epigenetic "poising" within articular cartilage. Future research should focus on confirming and expanding these findings to other environmental OA risk factors, including obesity, as well as determining late OA changes in mice.

Ear wound healing in MRL/MpJ mice is associated with gut microbiome composition and is transferable to non-healer mice via microbiome transplantation.

OBJECTIVE: Adult elastic cartilage has limited repair capacity. MRL/MpJ (MRL) mice, by contrast, are capable of spontaneously healing ear punctures. This study was undertaken to characterize microbiome differences between healer and non-healer mice and to evaluate whether this healing phenotype can be transferred via gut microbiome transplantation. METHODS: We orally transplanted C57BL/6J (B6) mice with MRL/MpJ cecal contents at weaning and as adults (n = 57) and measured ear hole closure 4 weeks after a 2.0mm punch and compared to vehicle-transplanted MRL and B6 (n = 25) and B6-transplanted MRL (n = 20) mice. Sex effects, timing of transplant relative to earpunch, and transgenerational heritability were evaluated. In a subset (n = 58), cecal microbiomes were profiled by 16S sequencing and compared to ear hole closure. Microbial metagenomes were imputed using PICRUSt. RESULTS: Transplantation of B6 mice with MRL microbiota, either in weanlings or adults, improved ear hole closure. B6-vehicle mice healed ear hole punches poorly (0.25±0.03mm, mm ear hole healing 4 weeks after a 2mm ear hole punch [2.0mm-final ear hole size], mean±SEM), whereas MRL-vehicle mice healed well (1.4±0.1mm). MRL-transplanted B6 mice healed roughly three times as well as B6-vehicle mice, and half as well as MRL-vehicle mice (0.74±0.05mm, P = 6.9E-10 vs. B6-vehicle, P = 5.2E-12 vs. MRL-vehicle). Transplantation of MRL mice with B6 cecal material did not reduce MRL healing (B6-transplanted MRL 1.3±0.1 vs. MRL-vehicle 1.4±0.1, p = 0.36). Transplantation prior to ear punch was associated with the greatest ear hole closure. Offspring of transplanted mice healed significantly better than non-transplanted control mice (offspring:0.63±0.03mm, mean±SEM vs. B6-vehicle control:0.25±0.03mm, n = 39 offspring, P = 4.6E-11). Several microbiome clades were correlated with healing, including Firmicutes (R = 0.84, P = 8.0E-7), Lactobacillales (R = 0.65, P = 1.1E-3), and Verrucomicrobia (R = -0.80, P = 9.2E-6). Females of all groups tended to heal better than males (B6-vehicle P = 0.059, MRL-transplanted B6 P = 0.096, offspring of MRL-transplanted B6 P = 0.0038, B6-transplanted MRL P = 1.6E-6, MRL-vehicle P = 0.0031). Many clades characteristic of female mouse cecal microbiota vs. males were the same as clades characteristic of MRL and MRL-transplanted B6 mice vs. B6 controls, including including increases in Clostridia and reductions in Verrucomicrobia in female mice. CONCLUSION: In this study, we found an association between the microbiome and tissue regeneration in MRL mice and demonstrate that this trait can be transferred to non-healer mice via microbiome transplantation. We identified several microbiome clades associated with healing.

DNA methylation and noncoding RNA in OA: Recent findings and methodological advances.

Introduction: Osteoarthritis (OA) is a chronic musculoskeletal disease characterized by progressive loss of joint function. Historically, it has been characterized as a disease caused by mechanical trauma, so-called 'wear and tear'. Over the past two decades, it has come to be understood as a complex systemic disorder involving gene-environmental interactions. Epigenetic changes have been increasingly implicated. Recent improvements in microarray and next-generation sequencing (NGS) technologies have allowed for ever more complex evaluations of epigenetic aberrations associated with the development and progression of OA. Methods: A systematic review was conducted in the Pubmed database. We curated studies that presented the results of DNA methylation and noncoding RNA research in human OA and OA animal models since 1985. Results: Herein, we discuss recent findings and methodological advancements in OA epigenetics, including a discussion of DNA methylation, including microarray and NGS studies, and noncoding RNAs. Beyond cartilage, we also highlight studies in subchondral bone and peripheral blood mononuclear cells, which highlight widespread and potentially clinically important alterations in epigenetic patterns seen in OA patients. Finally, we discuss epigenetic editing approaches in the context of OA. Conclusions: Although a substantial body of literature has already been published in OA, much is still unknown. Future OA epigenetics studies will no doubt continue to broaden our understanding of underlying pathophysiology and perhaps offer novel diagnostics and/or treatments for human OA.